Building performance modeling report xinxin hu by Xinxin Hu

48-524/722 BUILDING PERFORMANCE MODELING

Assignment #1: Revit Conceptual Energy Analysis

Group 2: Xinxin Hu Amber Jiang Akhil Mathur

Table of Contents Abstract .................................................................................................................................3 Building Information .............................................................................................................4 Climate of Pittsburgh, PA ...............................................................................................................4 Climate of Miami, FL ......................................................................................................................6

Baseline Model .....................................................................................................................8 Baseline Assumptions ....................................................................................................................8 Baseline Energy Performance Modeling .........................................................................................9 Energy Use ........................................................................................................................................... 9 Cost and CO2 Emissions....................................................................................................................... 13

Overall Comparison ............................................................................................................. 15 Pittsburgh ................................................................................................................................... 15 Miami ......................................................................................................................................... 15 Comparison between the best and the baseline models ............................................................... 16 Comparison with US National Benchmark .................................................................................... 17

Parametric Analysis in Pittsburgh, PA ................................................................................... 18 Building Form .............................................................................................................................. 18 Orientation ................................................................................................................................. 21 Window Area .............................................................................................................................. 21 Skylights ...................................................................................................................................... 23 Wall ............................................................................................................................................ 24 Roof ............................................................................................................................................ 26 Slab ............................................................................................................................................. 27 Glazing & Shading ........................................................................................................................ 27

Parametric Analysis in Miami, FL ......................................................................................... 28 Building Form .............................................................................................................................. 28 Orientation ................................................................................................................................. 32 Window Area .............................................................................................................................. 33 Skylight ........................................................................................................................................ 35 Wall ............................................................................................................................................ 36 Roof ............................................................................................................................................ 38 Slab ............................................................................................................................................. 38 Glazing & Shading ........................................................................................................................ 39

Conclusion ........................................................................................................................... 40 Bibliography ........................................................................................................................ 41

Abstract Conceptual Energy Analysis is conducted for a 3-story commercial office MRQ building using Autodesk Revit 2018, and its performance is simulated for Pittsburgh, PA and Miami, FL. A baseline model is initially designed using the layout and elevation data provided, with default parameters set by Revit. Using this as the baseline model, parametric analysis is conducted in multiple steps by individually modifying the building form, Window-to-Wall Ratio (WWR), opaque properties (wall, roof and slab) and transparent properties (window and glazing), and the best and worst cases are discerned based on the building annual Energy Use Intensity (EUI). These steps are carried out for both Pittsburgh and Miami, and the best case at every step is used as the reference model for the next one. The results obtained from Green Building Studio (GBS) are then compared and discussed, including their similarities and differences.

Building Information MQR is a 3-story commercial office building with a height of 11.5 m and a total floor area of 3,600 m 2. This study only included the grey office space shown in Figure 1. The building was modeled for both cold and hot climates in Pittsburgh, PA and Miami, FL, respectively.

Figure 1: 3D Rendition of MQR Office Building

Climate of Pittsburgh, PA Pittsburgh is considered a cold climate, meaning buildings need to have the capacity to heat during the winter (Office of Energy Efficiency & Renewable Energy, n.d.). Using Climate Consultant, Figure 2 was generated. As can be seen, Pittsburgh experiences temperatures below freezing 19% of the year and temperatures as high as 38 C. These temperatures would require an office building to have heating and cooling capacities. Pittsburgh has 5053 Heating Degree Days, confirming that Pittsburgh is a heating dominated location (International Code Council, 2006).

Figure 2: Climate Consultant Yearly Temperature for Pittsburgh, PA

Figure 3 shows the solar radiation for Pittsburgh, generated from Climate Consultant. The summer months have a higher radiation range, as expected. Increased solar radiation can increase cooling loads in the summer and can decrease heating loads in winter if the building is designed for passive heating and cooling.

Figure 3: Climate Consultant Yearly Solar Radiation Range for Pittsburgh, PA

Figure 4 shows the relative humidity for the year, generated from Climate Consultant. The spring months are the lowest humidity.

Figure 4: Climate Consultant Yearly humidity for Pittsburgh, PA

Climate of Miami, FL Miami is considered a hot and mid climate, meaning buildings need to have cooling capacity (Office of Energy Efficiency & Renewable Energy, n.d.). Using Climate Consultant, Figure 5 was generated. As can be seen, Miami never experiences temperatures below freezing and temperatures as high as 38 C. These temperatures would require an office building to have cooling capacities. Compared to Pittsburgh, the temperatures in Miami are much warmer year-round. Miami has only 214 Heating Degree Days, confirming that Miami is a cooling dominated location (International Code Council, 2006). Compared to Pittsburgh, which has 5053 Heating Degree Days, Miami is much warmer.

Figure 5: Climate Consultant Yearly Temperature for Miami, FL

Figure 6 shows the solar radiation for Miami. Compared to Pittsburgh in Figure 3, Miami has much higher solar radiation year-round, even in winter when Pittsburghâ&#x20AC;&#x2122;s solar radiation is much lower.

Figure 6: Climate Consultant for Yearly Solar Radiation for Miami, FL

Figure 7 shows the relative humidity for Miami, FL. Compared to Figure 4, Miami is much more mid year-round.

Figure 7: Climate Consultant for Relative humidity for Miami, FL

Baseline Model The first stage of the analysis involves designing a baseline model for the building and running simulation to obtain the default parameters and result for the EUI. We create the initial geometric model, using the given data for the building layout and elevation and use Revit to create the baseline model, choosing â&#x20AC;&#x2DC;officeâ&#x20AC;&#x2122; as our building type to meet the requirement. The results for the simulation work conducted in the report has been described in an easy to read manner, through extensive use of graphs and charts for representation purposes. These visualizations are used to then draw inferences about the two models in different locations. Comparisons are provided for the baseline, best and worst cases to study the impact of the parametric analysis on building performance.

Baseline Assumptions Revit assumes default settings for the constructed model, which form the baseline parameters for our model. The model assumes automatic thermal zoning with a default parameter of 3.6 mm. The Window-to-Wall Ratio is assumed to be 40%, which is acceptable for the baseline model, but should ideally not exceed this value. The Max Exterior Wall construction is assumed to be lightweight with mild climate insulation, which might not be suitable for a cold-climate city such as Pittsburgh but would be more suitable to Miami. The roof parameter is assumed to be Cool Roof with typical insulation, which would be more suited for a moderate to hot climate location, and not a cold location such as Pittsburgh. These Revit assumptions for the baseline model of both Pittsburgh and Miami, that would later be modified for the alternate scenarios, are stated in Tables 1 and 2.

Table 1: Revit default assumptions for baseline model

Total Percentage Glazing

40%

Total Percentage Skylights

Material Thermal Properties

Mass Exterior Wall

Lightweight Construction- Typical Mild Climate Insulation

Mass Interior Wall

Lightweight Construction- No Insulation

Mass Roof

Typical Insulation- Cool Roof

Mass Slab

High Mass Construction- No Insulation

Mass Glazing

Double Pane Clear- No Coating

Mass Skylight

Double Pane Clear- No Coating

Table 2: Building Summaries

Assumption

Pittsburgh, PA

Miami, FL

Number of People

145

142

Average Lighting Power Density (W/m2)

9.69

Average Equipment Power Density (W/m2)

14.42

Specific Fan Flow (L/s/m2)

5.6

5.0

Specific Fan Power (W/L/s)

-74,310.595

-83,966.265

Specific Cooling (m2/kW)

Specific Heating (m2/kW)

Total Fan Flow (L/s)

20,185

17,864

Total Cooling Capacity (kW)

-439,172

-439,183

Total Heating Capacity (kW)

439,602

Baseline Energy Performance Modeling The simulation results for both Pittsburgh and Miami baseline energy model are studied using Insight and Green Building Studio (GBS), and are results are discussed below:

Energy Use The energy use summary for the baseline model is described for both cities. In the parametric analysis in the following sections of the report, we attempt to optimize the model to reduce the EUI value and discern the best and worst cases in this manner.

Pittsburgh The summary for energy use in the Pittsburgh building is shown in Table 3. The EUI value for Pittsburgh baseline model is 713 MJ / mÂ˛ / year.

Table 3: Summary of Energy Use in Pittsburgh

Energy Use Intensity (EUI)

713 MJ / mÂ˛ / year

Electric

483,138 kWh

Fuel

842,896 MJ

Annual Peak Demand

137.1 kW

Figure 8: (Left) Energy and (Right) Fuel End use for Pittsburgh baseline model

Figure 8 shows the end use sectors for energy and fuel consumption. We observe that HVAC system consumes the highest energy (43.3%) and fuel (90.2%). Miscellaneous equipment consumes about 29% of the energy, followed by lighting (26.6%) and space cooling (21.5%). The major fuel consumption by HVAC is due to space heating (90%).

Figure 9 shows the building monthly energy use for the Pittsburgh case. We observe that the space heating energy use increases almost exponentially as the winter period approaches, which is a justifiable for a cold climate region such as Pittsburgh. The area lighting energy requirement is almost constant throughout the year. The space cooling, given in purple, sees a considerable increase during May-September. The lighting requirement decreases during winter, with an average consumption of 11.5% between December- February and an average of 23% between May and August, increasing during summer time. This can be explained because of the increased daytime in Pittsburgh during summers, which shortens considerably as winter approaches.

Figure 9: Pittsburgh Monthly Energy Use (MJ)

Miami The summary for energy use in the Miami building is shown in Table 4. The EUI value for the Miami baseline model is 596 MJ / mÂ˛ / year.

Table 4: Summary of Energy Use in Miami

Energy Use Intensity (EUI)

596 MJ / mÂ˛ / year

Electric

571,085 kWh

Fuel

61,385 MJ

Annual Peak Demand

143.1 kW

Figure 10: (Left) Energy and (Right) Fuel End Use for Baseline Model for Miami

Figure 10 shows the end use energy and fuel consumption for the Miami baseline case. Unlike Pittsburgh, HVAC consumes 52% of the total energy and 16.8% of the total fuel. The higher energy consumption relative to Pittsburgh is because of increased space cooling requirement, which account for 31% of the total energy consumption. This also explains the significant decrease in fuel consumption due to HVAC, as the space heating requirement is greatly reduced (16.8%), and most fuel consumed is used for hot water (83.2%). These results are justified by the hot and mid weather, which requires more cooling and minimal space heating.

Figure 11: Miami Monthly Total Energy Use (MJ)

Figure 3 shows the monthly energy use for the Miami baseline case. Lighting again remains constant, annually at about 21-23% of the total energy use. This is considerable average increase from the Pittsburgh results, but as Miami does not experience shortened daytime during winters, itâ&#x20AC;&#x2122;s expected. The monthly consumption comparison for Miami and Pittsburgh shows that Miami has a much lower energy consumption.

Cost and CO2 Emissions GBS provides us with information on the life cycle cost and the CO2 emissions, which can be used to evaluate the overall environmental and economic impact of the building. Pittsburgh The obtained results for the Pittsburgh building cost and emissions can be seen in Table 5.

Table 5:Cost and CO2 Emissions in Pittsburgh

Annual Energy Cost

$56,691

Lifecycle Cost

$744,889

Onsite fuel CO2 emissions

42,000 kg

Miami The obtained results for the Miami building cost and emissions can be seen in Table 6.

Table 6: Cost and CO2 Emissions in Miami

Annual Energy Cost

$58,187

Lifecycle Cost

$792,508

Onsite fuel CO2 emissions

3,100 kg

Comparing the above results for Pittsburgh and Miami, we see that Miami model performs slightly worse in terms of annual and overall life cycle cost, but Pittsburghâ&#x20AC;&#x2122;s fuel CO2 emissions are 14,000 times the Miami fuel CO2 emissions. This difference can be best explained by the fact that Pittsburgh and Miami have different fuel mix used for their electricity supply, which probably affects the total emissions calculated above (US Energy Information Administration , n.d.). This data is helpful in planning our greenhouse projects for our office building, as it gives us a good estimate of the baseline life cycle cost that we aim to reduce and helps in setting better carbon emission targets.

Overall Comparison Using Revit, a quick energy analysis could be performed by varying the important building properties- including thermal and structural parameters. Interesting results were obtained while conducting parametric analysis for slab properties, as neither the Pittsburgh nor the Miami model had any improvement on varying the slab schematic properties, with varying U-values. The closest parameters to the actual location were chosen for different stages of the parametric analysis, but lack of flexibility in Revit was experienced.

Pittsburgh For the best-case simulation, all aspects of comparison decreased except for the lifetime energy cost, as seen in Table 7. For the worst-case simulation, all aspects of comparison were increased, as expected. Table 7: Comparison of Energy Performance of Base, Best, and Worst Case in Pittsburgh

Base

Best

Best % Difference from Base

Worst

Worst % Difference from Base

EUI (MJ/m2 yr)

713

472

-33.8%

1685

+136.3%

Heating Load (% of Fuel)

90.2

69.5

-22.9%

97.6

+8.2%

Cooling Load (% of Electricity)

21.5

15.9

-26.0%

+39.5%

Lifetime Energy Cost ($)

744,889

560,724

-24.7%

1,378,870

+85.1%

Annual Energy Cost ($)

54,691

41,169

-24.7%

101,239

+85.1%

CO2 Emission (Mg)

13.4

-68.1%

162.4

+286.7%

Miami Table 8: Comparison of Energy Performance of Base, Best, and Worst Case in Miami

Base

Best

Best % Difference from Base

Worst

Worst % Difference from Base

EUI (MJ/m2 yr)

591.4

510

-13.8%

819

+38.5%

Heating Load (% of Fuel)

24.5

4.6

-8.1%

81.7

+233.5%

Cooling Load (% of Electricity)

30.7

26.9

-12.4%

36.9

+20.2%

Lifetime Energy Cost ($)

784935

690,636

-12.0%

1,062,568

+35.4%

Annual Energy Cost ($)

57631

50707

-12.0%

78015

+35.4%

CO2 Emission (Mg)

3.3

2.7

-18.2%

13.9

+321.2%

Comparison between the best and the baseline models •

Pittsburgh Table 9 Parametric Results for Baseline model and Best model (Pittsburgh)

•

Base

Best

Building Form

Original form as given

Form 3, Cuboid

Orientation

Same as given orientation

Same as Baseline

WWR

40%

20%

Skylight

Wall

Exterior Wall: Lightweight Construction Typical Mild Climate Insulation

Exterior Wall: Lightweight Construction – High Insulation

Interior Wall: Lightweight Construction No insulation

Interior Wall: High Mass Construction- No Insulation

Roof

Typical Insulation - Cool Roof

No insulation – Dark Roof

Slab

Un-insulated Solid (U=0.1243 BTU/(h·ft²·°F))

Glazing & Shading

Double Pane Clear – No Coating, No shading

Quad Pane Clear LowE Hot or Cold Climate, with Shading

Miami Table 10: Parametric Results for Baseline model and Best model (Miami)

Building Form

Baseline

Best

Simplified model from original

Form 3, Cuboid

Orientation

Same as given orientation

Same as Baseline

WWR

40%

20%

Skylight

Wall

Exterior Wall: Lightweight Construction Typical Mild Climate Insulation

Exterior Wall: Lightweight Construction – Typical Mild Climate Insulation

Interior Wall: Lightweight Construction- No insulation

Interior Wall: High Mass Construction – No Insulation

Roof

Typical Insulation - Cool Roof

Low Insulation - Cool Roof

Slab

High Mass Construction - No insulation

Glazing & Shading

Double Pane Clear No Coating, No shading

Double Pane Reflective, with Shading

Comparison and Discussion This project utilizes conceptual mass modeling in Revit to make modifications from listed eight aspects in Table n. As shown in Table 9 and Table 10, best models for Pittsburgh achieved 33.8% reduction on annual EUI and 13.8% reduction for Miami. From the reduction of annual EUI, annual Energy cost reduction could be achieved by 24.7% for Pittsburgh and 12% for Miami. The energy reduction is significant by using conceptual energy modeling and it could be utilized in early stage to roughly decide the building design. However, it is not feasible to apply detailed building energy modeling in Revit using conceptual mass modeling due to its limitations.

Comparison with US National Benchmark The results obtained from Revit simulation can be compared with the national benchmark value to assess the performance of our building. For benchmarking data, we use the Energy Star (created by the US EPA) Portfolio Manager’s ‘U.S. Energy Use Intensity by Property Type’ which has estimated the national median source EUI and is the recommended benchmark metric for all buildings in US (US Environmental Protection Agency, 2016). As recommended by the report, we use the source EUI value under the office category, which is given as 148.1 kBtu/ft2. This is equivalent to 1681 MJ/m2. Compared to our value, we see that the office satisfies and performs better than the benchmark value.

Parametric Analysis in Pittsburgh, PA The effects of four early design parameters on building performance are examined independently, which consists of building form, orientation, window to wall ratio and roof skylight, roof, wall, slab, glazing and shading. The annual energy use intensity (EUI) is considered as a main index for energy performance in the whole analysis. The parametric analysis also follows ASHREA 90.1 standard to fulfill the basic requirements.

Building Form Pittsburgh is a heating dominated city. The geometric form of the original building is simplified in the parametric analysis which is shown in the Figure 13. Based on the baseline model, four building forms are generated and investigated with the aim of testing which geometric model possesses the least EUI index. Some common geometry models are used to fully understand the impact of geometry changes on building energy performance. The total floor area, total number of floors and window to wall ratio are consistent across all the simulated building forms. The different geometric models and a simple description of the geometries are shown in Table 11.

Figure 12: Original Geometric form of MQR Building

Table 11: Building Form Details (Pittsburgh Simulations)

Graphic 3D view with Thermal zoning Form 1

Description Baseline Model Simplified from drawings Length:59.59m Width:22.55m

Form 2

Square model Length=width=34.73m

Form 3

Rectangular model Length:60m Width:20m

Form 4

Irregular model: Length: 59.59m Width:22.55m

Form 5

Hollow Square model Length:40.90m Width:35m Hollow square: Length=Width=15m

The total EUI of each building form is shown in Figure 14. The building form with the lowest EUI is the rectangular model (Form 3) with an annual EUI of 195.83 kWh/m2. Ts, Form 3 is passed to the next parametric analysis with varied orientation. The reason Form 3 has the lowest EUI is that it has a large south façade area and a relatively small east and west façade area, which reduces the heating load in summer and cooling load in winter. Also, among Forms 1, 3 and 4, Form 3 has the smallest SA:V, indicating that Form 3 has lower heat losses and heat gains. Although the square model (Form 2) has the minimum SA:V of 19%, its south façade area is the smallest among the 5 forms and has larger east and south façade areas. In summer, the east and west façade will experience excessive heat radiation, so reducing the area of the east and west façade is a good practice. Therefore, the square model (Form 2) has less solar radiation on the south façade compared to other models and its heating load increases significantly in the long winter of Pittsburgh.

Figure 13: Building Form Parametric Results

The parametric results indicated that maximizing the south faceing envelope area helps to take full advantage of winter sun exposure in Pittsburgh. In this way, heating load and lighting load in winter could be decreased. Ts, Form 3 is selected as the best performing model for the further analysis.

Orientation Based on the best performing Form 3, seven different orientation simulations are conducted. The building is rotated counter clockwise from 0 degree to 315 degrees. The parametric results shown below in Figure 15 indicate that 0-degree rotation and 180 degrees rotation models perform the best. In fact, 0-degree rotation model is the same as the 180 degrees rotation model because of its cuboid form.

Figure 14: Orientation Parametric Results (Pittsburgh, PA)

For Pittsburgh, winter solar heat gain is crucial to improve energy performance. The building with 0-degree rotation model takes advantage of passive and active solar strategies. In winter, its relatively large south faรงade collects, stores, distributes, and controls solar heat gains through the building mass. Meanwhile, daylighting from the south facade illuminates buildings during the day. In summer, smaller east and west facing faรงades reduces solar gain in the morning and afternoon. At the meantime, a south facing faรงade takes adequate daylight and solar gain for the building. Hence, building Form 3 with 0-degree rotation is taken as the best performing model for the future parametric analysis.

Window Area In the parametric analysis, the window to wall ratio is the window area percentage in the building. Window to wall ratio (WWR) is a crucial factor affecting thermal performance of the building. For Pittsburgh, heating accounts for the largest portion of energy consumption. Large glazing areas enable significant solar radiation in winter. Therefore, increasing WWR becomes our rule to investigate the impact of window area on building performance. The detailed modification plan and results are shown in Table 10. Table 12 Window Area Parametric Results (Pittsburgh, PA)

Window Area

Reasoning for modification

40%

Baseline Model

Annual EUI

Results Analysis

(kWh/m2/yr) 704.81

40% is the maximum WWR for building design.

20%

To investigate the impact of reducing 587.92 WWR.

EUI decreases around 17% from baseline model. (Best)

50%

To investigate the impact of increasing 769.72 WWR

EUI increases around 9% from baseline model.

ASHRAE 90.1 Standard

From the

In winter, Pittsburgh needs large window area to increase solar heat gain and decrease heating load. 60%

Further investigation of higher WWR

838.40

EUI increases around 19% from baseline model.

80%

Further investigation of higher WWR

965.02

EUI increases around 37% from baseline model. (Worst)

parametric results shown in Figure 16 and Table 12, a WWR of 20% performs best and significantly reduces annual EUI. The reason is that thermal insulation will become better with a lower WWR, even though high WWR allows significant solar gain through window. Good Heat insulation can be achieved with lower WWR and good heat insulation lead to low Heating, Ventilation and Air-Conditioning (HVAC) energy consumption. Hence, EUI increases as WWR increases, as shown in Figure 16.

Figure 15: Window Area Parametric Results (Pittsburgh, PA)

Further reduction of WWR is not be feasible considering aspects of maximizing natural view and daylight. Therefore, 20% is the minimum WWR that an office building could accept. To conclude, a window to wall ratio of 20% is the best scenario for future parametric analysis.

Skylights Skylights allow solar heat gain, daylighting, ventilation, natural view for buildings. The parametric analysis of skylight is similar to the window area analysis. Skylights introduce more direct solar radiation into the building and reduce heating load in winter and increase cooling load in summer. According to the ASHRAE standard (ASHRAE, 2010) , skylight percentage could not exceed 5% to gross roof area. In the modification of skylight percentage, high percentage of skylights are preferred to gain more solar heat and daylight.

Figure 16 Skylight Parametric Results (Pittsburgh)

However, Figure 17 indicates that annual EUI increases with increasing skylight percentage. EUI increased around 7% from the baseline model is observed for the 2% skylight case. Nevertheless, this is only the estimation of Revit using conceptual mass modeling, which may not reflect the real situation. Revit has its limitations and could not provide comprehensive and accurate energy performance. For the skylight parametric analysis, there are several reasons explaining why adding skylight causes an increase in EUI. First, Revit does not have dimming lighting devices in its simulation options so lighting consumptions stays same after adding skylights. Secondly, ventilation savings can not be reflected since Revit does not separate the ventilation system from the thermal system. Lastly, the natural view from skylights could increase productivity, however, productivity savings from employees is not be reflected by Revit and conceptual mass modelling. Hence, 2% skylight case with smallest EUI is selected for the future parametric analysis.

Wall For this parameter, we vary both the external and internal wall construction parameters under the conceptual types, in accordance with the climate of the city. Given the options between Lightweight and High Mass Construction with varying insulation parameters, we run multiple cases to understand the impact of the wall construction on the overall building EUI. We run 8 cases (including the baseline model) for both cities to obtain varying results. Note that we would not vary the Mass exterior wall (underground) parameter given in the options, as our building model does not include a basement. As mentioned previously, Pittsburgh can be defined as a cold climate city. This requires a certain degree of insulation in the office construction to store heating and provide thermal comfort to its occupants. For Pittsburgh, we focus on running more cases with relatively higher insulation, and compare those cases with running a few cases with high insulation. The different cases taken are shown in Table 13. Table 13: Wall Parametric analysis results for Pittsburgh, PA

S.No.

Mass Exterior Wall

Mass Interior Wall

Best Case Model EUI (MJ/m2/yr)

Worst Case Model EUI (MJ/m2/yr)

1 (Baseline)

Lightweight Construction Typical Mild Climate Insulation Lightweight Construction â&#x20AC;&#x201C; Typical Cold Climate Insulation

Lightweight Construction- No insulation

604.7

1493.8

Lightweight Construction â&#x20AC;&#x201C; No Insulation

576.4

1474.9

Lightweight Construction – No Insulation

831

1587.7

Lightweight Construction – High Insulation

Lightweight Construction – No Insulation

565.5

1469.9

Lightweight Construction – High Insulation

High Mass Construction- No Insulation

521.8

1368.9

High Mass Construction – High Insulation

High Mass Construction – No Insulation

535.7

1375.5

Light weight Construction – Typical Cold Climate Insulation

High Mass Construction – No Insulation

529.7

1374.4

High Mass Construction – Typical Cold Climate Insulation

High Mass Construction – No Insulation

535.7

1381.1

From the results, we see that the best case from the various wall alternative for Pittsburgh is for Case 5, i.e. Lightweight Construction (High insulation) and High Mass Construction (No insulation), with an EUI value of 521.8 MJ/m2/yr. Also, the alternatives using worst case model show that Case 3 gives the worst result, with an EUI value of 1587.7 MJ/m2/yr. The obtained results can be justified owing to the climatic parameters included in the model. Since we would require moderate to high insulation in the winters, hence the ‘high insulation’ for the exterior wall. At the same time, we would require enough release of excess heat during summer time. Since it is more challenging and energy consuming task to regulate the internal heat in a High Mass Construction, Light Weight Construction becomes the optimal choice during the summer period.

Similarly, for the worst-case scenario, we see that neither the exterior nor the interior wall have any insulation. This would lead to a considerable loss in heat during the winter period, which would largely increase the total energy cost of the building, and hence the EUI value. Ts, we use these cases to move forward with the roof parametric analysis.

Roof On obtaining the best and worst-case models for both Pittsburgh and Miami, we conduct further parametric analysis by varying the roof parameters. We run 7 different cases to test the differences in building performance. For our simulation, we again use the conceptual types under thermal properties, which allows us to test 7 alternatives. For this section, we use all available conceptual type roof options for both Pittsburgh and Miami. All the cases available in Revit are based on the concept of Dark Roof vs Cool Roof. A cool roof is one that can reflect most of the sunlight falling on it, absorbing less heat in the process1. This kind of roof is useful in moderate to hot climate where you would ideally want to save energy from air conditioning by keeping the roof heat out. On the other end, a dark roof can retain a large amount of heat falling on it, and can go up to 150 degrees Fahrenheit. This distinction is important as it might affect the final result obtained for the two contrasting cities, in terms of climate. Similar to the wall parameter, Pittsburgh would ideally have a moderate to highly insulated roof to have an optimal building performance. We test the different cases on both the best and worst models obtained from the previous results, and these cases are shown in Table 14: Table 14: Roof Parametric analysis results for Pittsburgh, PA

S.No.

Roof Case Typical Insulation - Cool Roof

Best Case model EUI (MJ/m2/yr) 521.8

Worst Case model EUI (MJ/m2/yr) 1587.7

1 2

High Insulation - Cool Roof

513

1576.2

3 4

High Insulation-Dark Roof Typical Insulation- Dark Roof

513 517.2

1574.4 1512.2

5 6 7

Low Insulation - Cool Roof Low Insulation - Dark Roof No Insulation - Dark Roof

541.4 541.2 760. 3

1535.9 1545.2 1684.9

From our results for Pittsburgh, we see that the best-case model become Case 3, i.e. a high insulation Dark Roof, with its EUI value of 513 MJ/m2/yr. Note that a high insulation cool roof also gives the same amount of EUI value, but we choose Case 3 as our best option because of its lower fuel consumption.

https://energy.gov/energysaver/energy-efficient-home-design/cool-roofs 26

Slab Different slab options have different U values, or the rate of heat transfer. Theoretically, the lower the U value, the lower the rate of heat transfer, which would decrease the heating load in the winter and cooling load in the summer. This is seen in the worst case, with the Un-insulated solid slab option with a U value of U=0.1243 BTU/(h·ft²·°F), which is the highest U value of the simulated options, as seen in Table 15. However, in the bestcase simulations for Pittsburgh, also seen in Table 15, the highest U value had the best EUI. This does not follow the previously stated theory that the lower U values would have higher EUI’s. This could indicate the limitations of Revit, which cannot create models with the accuracy of real life situations. Table 15: Slab Options for Pittsburgh, PA

Schematic Slab Type

Best Case EUI (MJ/m2/yr)

Worst Case EUI (MJ/m2/yr)

Un-insulated solid (U=0.1243 BTU/(h·ft²·°F))

513

1684.9

Super-insulated (U=0.0485 BTU/(h·ft²·°F))

686.2

1284.7

Slab edge R-5 insulation (U=0.0264 BTU/(h·ft²·°F))

700

1341.7

Standard slab construction - A (U=0.0440 BTU/(h·ft²·°F))

959.1

1510.4

Suspended timber floor (U=0.0679 BTU/(h·ft²·°F))

833.1

1442.8

Slab edge R-15 insulation (U=0.0194 BTU/(h·ft²·°F))

700

1341.7

Un-insulated suspended timber (U=0.1106 BTU/(h·ft²·°F))

833.1

1442.8

From these results, we chose case 1 to be the best and worst cases to continue on the parametric analysis.

Glazing & Shading Glazing is an important aspect in heat transfer of a building with the exterior environment. Since heat transfers much more easily through windows than walls, the type of glazing can help combat the loss of heat. In the Pittsburgh simulations, the default glazing option was a double pane clear glass with no coating. This is a cheap and standard option, but allows a significant portion of heat to escape. The more panes there are, the better insulated the building becomes, but the cost of installation and capital costs increase substantially. Therefore, the Quad Pane Clear option had the lowest EUI. Additionally, the Single Pane Option has the highest EUI, showing that it allows the most heat to escape. Ts, the heating load during winter would increase, causing the EUI to increase. Since double pane glass is the most economical, there are several different types of double paned glass that try to address the issue of heat loss. The different types of coatings are all aimed to decrease heat loss without adding additional panes. They all perform better than the base default with no coating, but

not as well as the triple or quad paned glass. All the options used for cold climate Pittsburgh and the resulting EUI’s can be seen in Table 16.

Table 16: Glazing Options for Pittsburgh, PA

Conceptual Glazing Type

Best Case EUI (MJ/m2/yr)

Worst Case EUI (MJ/m2/yr)

Double Pane Clear No Coating

513.0

1684.9

Double Pane Clear LowE Cold Climate, High SHGC

494.2

1601.3

Double Pane Clear High Performance, LowE, High Tvis, Low SHGC

488.1

1333.8

Triple Pane Clear LowE Hot or Cold Climate

481.8

1408.1

Quad Pane Clear LowE Hot or Cold Climate

475.8

1378.3

Single Pane Reflective

531.5

1473.9

Double Pane Reflective

506.8

1433.7

The glass can also be shaded, which could potentially decrease the cooling load during the summer but also increase the heating during the winter. Compared to the best case in Pittsburgh without shading, the EUI decreased by 4 MJ/m2/yr, which indicates that the cooling load was decreased more than the heat was increased in the winter.

Parametric Analysis in Miami, FL Building Form Miami is a cooling dominated city and its weather condition is very distinct than Pittsburgh’s. It is hot and mid during its long summer period. The building form analysis follows Climate Consultant’s strategies listed below: • • • •

Minimize or eliminate west facing glazing to reduce summer and fall afternoon heat gain Long narrow building floorplan can help maximize cross ventilation in temperate and hot mid climates Flat roofs work well in hot dry climates (especially if light colored) Decreasing west façade area provides a good opportunity to reduce west facing glazing.

Table 17 Building form details (Miami, FL)

Graphic 3D view with Thermal zoning Form 1

Form 2

Reasoning for Modification • Baseline Model (tried to model the original MRQ building)

Annual EUI & Result Analysis 591.01 MJ/m^2/yr

697.90 MJ/m^2/yr • Small SA:V; • Reduce heat loss ▪ EUI rises by about 18% in winter and from the heat gain in baseline model summer because of relatively large

west and east facing façade

Form 3

• Reduce west and east façade area • Reduce solar radiation on west and east facade

577.19 MJ/m^2/yr ▪ EUI decreases about 2.3% from the baseline model ▪ Best Performance: because it has the smallest west and east façade area and its long narrow form increases cross ventilation

Form 4

• Reduce west façade area and change its orientation to Northwest • Reduce solar radiation

591.41 MJ/m^2/yr ▪ Similar EUI with form 3 because of elimination of west façade solar gain.

Form 5

• Orient west and east facade to Southwest and Southeast respectively • Increase North Façade area to reduce solar radiation on the south, east and west facade

584.32 MJ/m^2/yr ▪ EUI decreases, similar performance with form 3. ▪ Relatively large north façade reduces solar gain ▪ its long narrow form increases cross ventilation

The results from Table 17 show that reducing west & east facing façade area and increasing north facing façade area could reduce energy consumption for buildings in Miami. A long and narrow building form could also help reduce energy consumption through cross ventilation. However, Revit energy analysis with conceptual masses has defaulted assumption for ventilation, so the energy reduction on the HVAC system cannot be shown in this early design stage. More specific energy modeling could be fulfilled in EnergyPlus. The EUI’s are also shown graphically in Figure 18. The main differences between all the modified forms are facade area and façade orientation. The best performance building form (Form 3) possesses the smallest west and east façade area and large north façade area. The form reduces solar gain in summer according to its solar path. Ts, Form 3 is selected as the best performing model for the further analysis.

Figure 17 Building form parametric results (Miami, FL)

Orientation Based on the best performing building Form 3 (rectangular), seven different orientation simulations are conducted. The building is rotated counter clockwise from 0 degree to 315 degrees. Similar to the parametric results in Pittsburgh, the parametric results shown below in Figure 19 indicate that the 0-degree rotation and 180degree rotation models perform best. In fact, 0-degree rotation model is the same as the 180-degree rotation model because of its cuboid form.

Figure 18 Orientation Parametric Results (Miami, FL)

For a hot climate like Miami, it is ideal to reduce solar heat gain in summer. The building with 0-degree rotation model takes advantage of passive and active solar strategies. In summer, relatively small east and west façades reduce solar gain in the morning and afternoon. At the meantime, south facing façades have adequate daylight and solar gain for the building. Hence, building Form 3 with 0-degree rotation is taken as the best performing model for the future parametric analysis.

Window Area According to climate consultant’s strategy and Miami’s hot and mid climate, the building should be orienting most of the glass to the north. There are essentially no passive solar needs for Miami because of its hot and mid climate. All Miami buildings need cooling capacity so buildings need to reduce solar gain to reduce cooling load. Ts, low window to wall ratios under 40% are simulated to find the trend of impact on energy performance as shown in Table 18. As shown, orienting most of the glass to north façade and limiting glazing area of west façade is the strategy to limiting solar heat gain through glazing. Window Area

Reasoning for modification

Total WWR-

40%

Baseline Model

North-

40%

•

South-

40%

West-

40%

East-

40%

Total WWR-

30%

North-

45%

South-

20%

West-

10%

East-

30%

Total WWR-

20%

North-

27%

Annual EUI

Results Analysis

(MJ/m^2/yr) 40% is the maximum WWR for building design.

ASHRAE 90.1 Standard 577.2

•

EUI decreases around 7% from baseline model.

To investigate the impact of reducing WWR. 534.8

•

Further investigation of lower WWR

523.2

EUI decreases around 9% from baseline model.

South-

20%

West-

10%

East-

10%

Total WWR-

10%

North-

10%

South-

10%

West-

10%

East-

10%

Table 18

•

EUI decreases around 11% from baseline model.

Further investigation of lower WWR 511.5

Total WWR-

50%

North-

50%

South-

50%

West-

50%

East-

50%

•

EUI increases around 4% from baseline model.

To investigate impact on increasing WWR 602.2

Altering window areas on each façade (Miami, FL)

The result shown in Table 18 shows that lower WWR ratio on the west façade and higher WWR on the north façade could slightly reduce annual EUI. Window to wall ratios of 10% performs the best, but 10% is not acceptable considering natural view and daylight. From Figure 20, the reduction of EUI is not obvious from 30% to 20% and even lower to 10%. On the other hand, a WWR of 20% is the minimum WWR that an office building could accept. Hence window to wall ratio of 20% is the best scenario for future parametric analysis.

Figure 19 Window Area Parametric Results (Miami, FL)

According to Figure 20 and Table 18, when WWR is modified from 40% to 20%, impact on annual EUI is only reduced by 7%. Comparing to the 17% reduction in Pittsburgh’s WWR parametric results, glazing percentage does not have much impact on energy performance. The reason might be the humidity requirement of indoor air. Miami’s air is very mid and Pittsburgh’s air is rather dry. Although solar heat gain decreases through smaller window areas and dry bulb temperature decreases, humidity of fresh air in the HVAC system needs to be treated to be a comfortable value. In this case, air often needs to be overcooled and reheated to satisfy the requirements of both humidity and temperature. This process consumes a large amount of energy. Therefore, the impact of WWR on annual EUI is not as distinct as Pittsburgh’s. The result shown in Table 18 shows that lower WWR ratios on the west façade and higher WWR on the north façade could slightly reduce annual EUI. A WWR of 10% performs the best, but 10% is not acceptable considering natural view and daylight. From Figure 20, the reduction of EUI is not obvious from 30% to 20% and even lower when compared to 10%. On the other hand, a WWR of 20% is the minimum WWR that an office building could accept. Hence window to wall ratio of 20% is the best scenario for future parametric analysis.

Skylight For Miami, skylight percentage should be kept as small as possible to limit solar heat gain. The skylight percentages planned are all smaller that 5%. Similar to Pittsburgh’s case, Figure 21 indicates that annual EUI increases when increasing skylight percentage. However, the result does not reflect the real building energy performance since conceptual mass modeling has its own limitations. Therefore, the 1% skylight was chosen as the best case for future parametric analysis. In this way, skylights may not affect annual EUI much, only about 0.9% rise of EUI according to Revit.

Figure 20 Skylight Parametric Results(Miami)

Wall Being a hot and mid climate city, the wall parameters for Miami would be varied to ensure that there is an overall low insulation in the building, allowing unnecessary heat to escape. Due to this, more focus was placed on simulating cases with relatively low insulation values. The cases considered for Miami differ from Pittsburgh due to this reason and the summary of the simulation cases is shown in Table 19. Table 19: Wall Parametric Analysis Results for Miami, FL

S.No.

Mass Exterior Wall

Mass Interior Wall

Best Case Model EUI (MJ/m2 /yr)

Worst Case Model EUI (MJ/m2 /yr)

1 (Baseline)

Lightweight Construction Typical Mild Climate Insulation Lightweight Construction â&#x20AC;&#x201C; Low Insulation

Lightweight Construction- No insulation

527.9

775.9

Lightweight Construction â&#x20AC;&#x201C; No Insulation

528.1

767.2

High Mass Construction – High Insulation

Lightweight Construction – No Insulation

523.7

764.5

High Mass Construction – No Insulation

Lightweight Construction – No Insulation

534.8

725.2

High Mass Construction – Typical Mild Climate Insulation

Lightweight Construction – No Insulation

525.6

761.1

High Mass Construction – No Insulation

535.6

714.2

Lightweight Construction – No Insulation

High Mass Construction – No Insulation

543.4

785.7

Lightweight Construction – Typical Mild Climate Insulation

High Mass Construction – No Insulation

523.5

756.1

From the above results, we observe that Case 8 (Lightweight Construction-Typical Mild Climate Insulation and High Mass Construction-No insulation) and Case 7(Lightweight Construction-No insulation and High Mass Construction- No insulation) give us the best and worst case scenarios with EUI values of 523.7 MJ/m 2/yr and 785.7 MJ/m2/yr respectively. From the results for Miami, we see that the building is best optimized in lightweight construction with mild climate insulation, which aligns with our reasoning for simulating cases compatible for Miami climate (Typical Mild Climate Insulation is the closest option possible to Miami climate). The result for the worst case is unexpected, as it was expected for case 6 to have higher energy usage. This is because High Mass construction used in case 6 was

expected to consume more energy to release the internally stored heat in a hot climate, when compared to Lightweight Construction.

Roof We conduct similar rounds of simulation with the best and worst Miami model files obtained from the wall analysis results. We again run the cases for the biggest and worst case models, and the results obtained as shown in Table 20. Table 20: Roof Parametric analysis results for Miami, FL

S.No.

Roof Case Typical Insulation - Cool Roof

Best Case model EUI (MJ/m2/yr) 523.5

Worst Case model EUI (MJ/m2/yr) 785.7

1 2

High Insulation - Cool Roof

523.1

783.7

3 4

High Insulation-Dark Roof Typical Insulation- Dark Roof

525.1 524.8

784.8 762.8

5 6

Low Insulation - Cool Roof Low Insulation - Dark Roof

522.8 528.7

765 773.2

No Insulation - Dark Roof

581.7

818.9

From the Miami results, we see that the best alternative is Case 5, i.e. Low Insulation- Cool Roof with an EUI value of 522.8 MJ/m2/yr, and the worst-case alternative is Case 7, i.e. 818.9 MJ/m2/yr. Similar to Pittsburgh, the bestcase roof case for Miami is justified as you would ideally want a cool roof with less insulation to release as much heat as possible. As Miami would remain moderate- hot on most days, it would be optimal for the building to have a cool roof design with least possible insulation.

Slab Similar slab options were used to model Miami. The default option of High Mass Construction â&#x20AC;&#x201C; No Insulation slab gave the best and worst EUI for the best and worst cases. As seen in Table 21, the EUI difference between the no insulation and cold climate insulation was negligible because the building was modeled in Miami, which is a hot and mid climate. Table 21: Slab Options for Miami, FL

Conceptual and Schematic Slab Type

Best Case EUI (MJ/m2/yr)

Worst Case EUI (MJ/m2/yr)

High Mass Construction - No insulation

522.8

818.9

High Mass Construction - Cold Climate Insulation

522.8

818.9

Un-insulated solid (U=0.1243 BTU/(h·ft²·°F))

567.3

777

Slab edge R-5 insulation (U=0.0264 BTU/(h·ft²·°F))

571.6

798.6

Standard slab construction - A (U=0.0440 BTU/(h·ft²·°F))

567.3

777

Standard slab construction - B (U=0.0716 BTU/(h·ft²·°F))

567.3

777

Slab edge R-20 insulation (U=0.0176 BTU/(h·ft²·°F))

571.6

798.6

From these results, the first case was chosen to continue on the parametric analysis.

Glazing & Shading Similar glazing options were used for Miami. The options used for hot climate Miami and the resulting EUI’s can be seen in Table 22. The best performing option was the Double Pane Reflective option. Since the climate is hot and mid, there is not much need to worry about heat loss in the winter. In fact, the more prevalent issue would be heat gain in the summer, which would increase cooling loads. Since the glazing on the glass is reflective, this option would decrease the amount of external heat that entered the building. Interestingly for the worst case, the Double Pane Clear No Coating option gave the worst EUI out of the options, even worse than the Single Pane Reflective option. This might be because the reflective properties of the single pane are able to divert some heat during the summer, which would help to decrease cooling loads. Table 22: Glazing Options for Miami, FL

Conceptual Glazing Type

Best Case EUI (MJ/m2/yr)

Worst Case EUI (MJ/m2/yr)

Double Pane Clear No Coating

522.8

818.9

Double Pane Clear Tinted

520.2

795.3

Double Pane Reflective

509.8

699.2

Double Pane Clear LowE Hot Climate, High SHGC

515.1

741.6

Double Pane Clear High Performance, LowE, High Transmittance, Low SHGC

510.4

696.5

Triple Pane Clear LowE Hot or Cold Climate

516.5

758.5

Conclusion Revit CEA was used for a preliminary energy performance modeling of a MRQ building in Pittsburgh. Our final best case gives us an improvement of 33.8% in EUI value for Pittsburgh and 32.8% improvement for Miami. For both Pittsburgh and Miami, we observed that optimizing the WWR value has the most significant impact on improving the energy performance of the building. Though this analysis assists in identifying the key areas for initial improvement, further modeling and analysis is required to get more concrete results, as Revit only provides a quick and rough simulation.

Bibliography ASHRAE. (2010). ASHSSRAE Standard 90.1 2010: Energy Standard for buildings except low rise residential buildings SI edition, Atlanta, GA, USA: ASHREA. International Code Council. (2006). Appendix D: Degree Day and Design Temperatures. Virginia Plumbing Code. International Code Council. Office of Energy Efficiency & Renewable Energy. (n.d.). Guides and Case Studies for All Climates. Retrieved September 18, 2017, from energy.gov: https://energy.gov/eere/buildings/guides-and-case-studies-allclimates

48-524/722 BUILDING PERFORMANCE MODELING

Assignment #2 eQUEST Energy Parametric Analysis

Group 2: Amber Jiang Xinxin Hu Akhil Mathur

Table of Contents Abstract 1. Climate of Pittsburgh 2. Introduction 2.1 Building Information 3. Baseline Model Comparison: REVIT-CEA v/s eQUEST 3.1 Default Inputs and Assumptions 3.2 Performance Comparison 3.3 Comparing the capabilities of REVIT and eQUEST 4. New Baseline Model - eQUEST 5. Parametric Simulations Configuration of Parametric Analysis 5.1 Envelope Thermal Properties 5.1.1 Wall Material 5.1.2 Roof Material 5.1.3 Slab Material 5.1.4 Glazing Material 5.2 Internal Loads 5.2.1 Occupancy Density (OD) 5.2.2 Lighting Power Density (LPD) 5.2.3 Equipment Power Density (EPD) 5.3 Scheduling 5.3.1 Occupancy Schedule 5.3.2 Equipment Schedule 5.3.3 Lighting Schedule 5.4 Daylighting 6. Baseline v/s Final Proposed Design 7. Benchmark Comparison Conclusions Bibliography

Abstract The study provides a building performance analysis for a 3-storey medium sized MRQ office building, with a floor are of 3,600 m2 and located in Pittsburgh. The results previously obtained from Revit are used as reference, and the baseline model is imported to eQUEST to compare the pros and cons of their respective default assumptions. Further modifications are made to eQUEST baseline model so that it is in compliance with the latest ASHRAE 90.1 standards. The default assumptions obtained in Revit and eQUEST are compared and studied. Using the modified baseline as the reference, parametric analysis is carried out for the building properties to understand their effect on the overall building energy use performance. The order for conducting this analysis starts with the building envelope properties- External wall, roof, slab and glazing, choosing the best-case scenario for analysis at every step. The second stage involves analysis for the internal load- lighting power density, equipment power density and occupation density, followed by modifying the office scheduling- occupancy schedule, equipment schedule and lighting schedule. Moving forward with the model with the best Energy Use Intensity (EUI) at every step, a final best-case model is obtained, with an EUI reduction of 18.9%. It is observed that reduction of Equipment Power Density significantly impacts on EUI.

The performance analysis helps us to understand key modeling differences between Revit and eQUEST, and their individual pros and cons. We observe that eQUEST allows us to design more detailed building scenarios, by having the flexibility to vary the U-value, occupancy schedules, lighting power density, lighting schedule, equipment power density, and equipment schedules. Detailed results are also obtained for the energy consumption end-use sectors in eQUEST, which provides the monthly and annual data for electricity use and gas energy consumption in the building.

1. Introduction A detailed energy performance analysis is conducted for a 3-storey medium-sized office MRQ building with a floor area of approximately 3,600 m2. With the building location in Pittsburgh, a conceptual energy model is first developed using Autodesk Revit. The same file is imported and simulated in eQUEST and the default parameter assumptions in Revit and eQUEST are compared, to study their pros and cons. The obtained energy model parameters in eQUEST are compared with ASHRAE 90.1.2016 standards and the required modifications are made to obtain a modified baseline complying with the latest ASHRAE standards for non-residential buildings. eQUESTâ&#x20AC;&#x2122;s default HVAC system allocated an air handling unit (AHU) to each thermal zone. This is unnecessary, so the AHU systems were reallocated with one system per floor. The thermal zones are created through the core/perimeter standard, as explained in the following sections. Parametric analysis is then conducted for the model on eQUEST by modifying the thermal envelope properties (external wall, roof slab, glazing and skylight), internal load (lighting power density, equipment power density and occupation density), office scheduling (occupancy schedule, equipment schedule, lighting schedule) and daylighting properties for the building. This is done in the given order to study the impact of each parameter on the overall building consumption, and consequently identify the key areas of improvement in the building. All parameters and obtained results are given in IP units.

Key Objectives

1. To highlight the differences between default parameter assumptions in Revit and eQUEST and study their pros and cons 2. To study the impact of ASHRAE standard compliance on baseline energy model and compute the energy consumption improvement (if any) 3. To conduct parametric analysis for the building thermal envelope properties on eQUEST to understand any potential correlation between the envelope U- value and overall energy consumption of the building 4. To modify the envelope, internal load, scheduling and daylighting properties and develop the best-case model for the building in terms of its final energy usage (electricity use and gas consumption) 5. To compare the best-case eQUEST model obtained with the previously developed best case model on Revit and study their differences 6. To compare the final best-case eQUEST model with the baseline e-Quest model and identify the impact of the different building parameters on the total Energy Use Intensity (EUI) 7. To determine the final end-use sectors for the electricity use and gas consumption in the building and highlight the major contributors to the building Energy Use Intensity (EUI) 1.1 Building Information

Thermal zoning of the MRQ building is based on default set of REVIT with perimeter depth of 15ft. We recreated detailed floor plan (shown in Figure 2,3,4) based on floor layout and room usage of the entire building provided. MRQ is a 3-story commercial office building with a height of 11.5 m and a total floor area of 3,619 m2. This study only included the grey office space shown in Figure 1. The building was modeled in Pittsburgh, PA with cold climate dominated weather.

Figure 21 1st Floor Plan of MRQ building

Figure 22 MRQ building 3D Model

Figure 23 2nd Floor Plan of MRQ building Figure 24 3rd Floor Plan of MRQ building

2. Climate Pittsburgh is considered a cold climate, meaning buildings need to have the capacity to heat during the winter [1]. Pittsburgh experiences temperatures below freezing and temperatures as high as 100 F. These temperatures would require an office building to have heating and cooling capacities. Figure 5 shows the average dry bulb temperatures per month in Pittsburgh, PA, generated by Climate Consultant.

Figure 25: Average Dry Bulb Temperature in Pittsburgh, PA Figure 6 shows the average relative humidity for each month with a peak humidity in September and a low humidity in April. With high humidity, HVAC systems will need to ensure enough moisture is taken out of the air to avoid mold growth and keep the comfort of occupants at an acceptable level.

Figure 26: Average Relative Humidity in Pittsburgh, PA Figure 7 is taken from Climate Consultant and shows the directionality of wind in the form of a wind rose. The max wind speeds reach 30 mph, mostly from the west direction, and minimum wind speeds of 6 mph from the northeast direction. The directionality of wind impacts energy efficiency of a building based on its orientation. If the side with a large surface area is facing the side with higher wind speeds, there will be more infiltration, which will drive up heating and cooling costs.

Figure 27: Wind Rose in Pittsburgh, PA (Climate Consultant) Figure 8 indicates the average wind speeds for each month, taken from climate consultant. Wind speeds are higher in the winter months, which impacts the heating loads because of infiltration through the building envelope. Especially if the building is not optimally oriented, higher wind speeds in winter months could drastically increase the heating cost as well as cause drafts through the building, which negatively impacts occupancy comfort.

Figure 28: Average Wind Speed in Pittsburgh, PA

ASHRAE 90.1 classifies Pittsburgh as part of climate zone 5A, meaning it is cold and moist, with similar climates as Cleveland, Boston, Denver, Detroit, and Chicago. Thus, all parametric simulation choices are made with the fact that heating is a much larger component of energy use than cooling. In general, the more insulated the building, the more heating the building will save.

3.1 Default Assumptions in Revit and eQUEST Once the baseline energy model on Revit is developed, it can be exported to Green Building Studio (GBS). The GBS interface allows the energy model to be imported as an inp file, which could be run on eQUEST. Following this process, the baseline model is obtained on eQUEST, with its default parameter assumptions re-defined. The table below compares these default assumptions for the baseline model as obtained in Revit and eQUEST.

Roof Revit

eQUEST

Type

Typical Insulation- Cool Roof

Insulation entirely above the deck

Construction

4 in lightweight concrete

0.031 feet Built up roof 3.8 inch+ 0.500 feet Min-bld 3-inch +0.063 feet Wood Sft ¾ inch

U-Value (BTU/(h·ft²·°F)

0.2245

0.044

Revit

eQUEST

Type

Lightweight Construction – Typical Mild Climate Insulation

R13 Wood Frame

Construction

8 in lightweight concrete block

0.058 feet Wood Shingle, 0.063 feet Wood Sft ¾ inch, 0.052 feet Gypsum Board 5/8in

U-Value (BTU/(h·ft²·°F)

0.1428

0.078

Revit

eQUEST

External Wall

Glazing

Type

Double Pane Clear – No Coating

Double Glazing Clear Pane

SHGC

0.13

0.604

U-Value (BTU/(h·ft²·°F)

0.5145

0.536

Visible Transmittance

0.781

Revit

eQUEST

Lighting Power Density

No data

1.02 W/ft2

Equipment Power Density

No data

1 W/ft2

Occupancy

No data

100 ft2 per person

Internal Loads

Clearly, there is some default data provided by eQUEST which is not available on Revit. This includes the internal load data, which does not seem to be available on Revit, but has pre-defined values on eQUEST. There is some clear difference in the U-values defined by the 2 software for the building envelope, where eQUEST sets lower U-values, resulting in higher insulation. The envelope construction type remains similar for both software, though eQUEST provides us a more detailed description of the properties and the insulation material used.

3.2 Performance Comparison After exporting the REVIT baseline file into eQUEST, eQUEST automatically assigns one AHU system per thermal zone per floor, resulting in 15 AHUs for MRQ. However, as this is unrealistic, the AHUs were allocated into a more reasonable distribution of one AHU per floor, resulting in 3 AHU systems. From there, the baseline model was run in eQUEST and the results are found in the table below compared to the results found from running the model in REVIT. As can be seen, the energy consumption values are very similar, which makes sense as the REVIT model uses the same DOE engine in the background away from user interface. Table 22: Overall Energy Comparison between REVIT and eQUEST Baseline Model REVIT (MMBtu/yr) eQUEST (MMBtu/yr) Percent Difference Total Annual Energy Consumption 2,447.48

2,485.56

-1.6%

Annual Electricity Consumption

1,589.38

3.6%

1,648.57

Annual Gas Consumption

798.91

896.18

-12.2%

The REVIT results split annual electricity end use into three categories: HVAC, Lighting, and Other. eQUEST splits the annual electricity end use into more detailed categories including, but not limited to, space heating, space cooling, ventilation fans, pumps, miscellaneous equipment, area lights, and task lights. By grouping the HVAC and lighting categories, with all remaining categories grouped into Other, the comparison between REVIT and eQUEST can be seen below. Again, the percentages of total electricity usage per year are very similar between REVIT and eQUEST. Table 23: Electricity Consumption Comparison between REVIT and eQUEST Baseline Model REVIT (% of electricity) eQUEST (% of electricity) HVAC

43.3%

41.2%

Lighting 29.9%

27.8%

Other

31%

26.8%

The REVIT results split annual fuel end use into two categories: HVAC and Other. eQUEST splits the annual fuel end use into more detailed categories that are the same as the categories in annual electricity end usage. However, only space heating and hot water have usages. The comparison between REVIT and eQUEST can be seen below. Again, the percentages of total fuel usage per year are very similar between REVIT and eQUEST. Table 24: Gas Consumption Comparison between REVIT and eQUEST Baseline Model REVIT (% of Fuel) eQUEST (% of Fuel) HVAC

90.2%

Other 9.8%

91.3% 8.7%

3.3 Comparing the capabilities of REVIT and eQUEST Revit is a user-friendly software to grasp a general idea of energy performance of buildings and only for early stage design, while eQUEST provides a strong interface for designers to set envelope parameters and model internal loads and schedules. However, eQUEST may have trouble in handling complex building models transferred from Revit or other design software. eQUEST is also capable to modeling daylight and lighting which is very important for sustainable design. Detailed comparisons are shown in the following table. Aspects

Revit

eQUEST

Material and Constructions

-Cons: limited fixed material assembly choices

-Pros: large database of material assembly selection with modifiable layers

Glazing

-Cons: only 11 glazing assembly choices

- Pros: a broad Library for glazing choices with detailed index

Schedule

- Cons: cannot modify schedule information

- Pros: able to modify detailed scheduling at hour level.

Simulation

- Cons: could run simulation online but detailed simulation files process time are long

- Pros: able to simulate whole building information with HVAC system within a short period

Simulation result

- Cons: Insight 360 provides design strategies

- Pros: detailed report regarding to every parameter and provide hourly report for statistical analysis.

-provides annual EUI -benchmarking comparison for each case and comparison between cases Flexibility in Units

- Pros: Both SI and IP

-Cons: only IP

File Interoperability

- Cons: the model file could only be opened in Revit, files from AutoCAD can’t be interpret well

- Pros: can only file from Revit and other compatible software

Interface

- Pros: easy to use with explicit introduction and tutorials

- Cons: complicated to use and can’t modify mass name

Display of 3D model

- Pros: easy to rotate and observe detailed view

- Cons: difficult to rotate the and enlarge camera view of 3d model

4. New Baseline Model – eQUEST The baseline model that was exported directly from Green Building Studio had several defaulted values that were not up to ASHRAE standards. Since ASHRAE is used in building construction and operations in the United States, a new baseline model was developed to bring all the values up to ASHRAE standards since the MRQ

building is located in Pittsburgh, PA. The new parameters were based on the MRQ building being located in Climate Zone 5A in the ASHRAE 90.1 standard. The following changes were made to the baseline model imported from GBS: •

Roof- Thickness of ‘mat-247’ changed to 0.785 from 0.031 feet

•

Wall- Thickness of ‘mat-384’ changed to 0.550 from 0.063 feet

•

Glazing- double low-e glazing with paint

•

Occupancy – changed from 100 m2 per person to 275 m2 per person

•

Lighting power density- changed from 0.9 W/m2 to 1.02 W/m2

•

Equipment power density: changed to 1 W/ft2 from 1.34 W/ft2

•

Occupancy Schedule and Lighting Schedule (ASHRAE 90.1 Appendix G):

Figure 29: Occupancy and Lighting Schedule (ASHRAE 90.1 Appendix G) Tables 4, 5, 6 and 7 show the changes that were made to the default model. For the remainder of the report, whenever the baseline model is referred, this new ASHRAE compliant model is the new baseline.

Table 25: Roof Baseline Changes eQUEST Default

ASHRAE Standard

Type

Insulation entirely above the deck

Construction

0.031 feet Built up roof 3.8 inch+ 0.500 feet Min-bld 3 inch +0.063 feet Wood Sft ¾ inch

U-Value (BTU/(h·ft²·°F)

0.044

0.032

Table 26: External Wall Baseline Changes eQUEST Default

ASHRAE Standard

Type

R13 Wood Frame

Construction

0.058 feet Wood Shingle, 0.063 feet Wood Sft ¾ inch, 0.052 feet Gypsum Board 5/8in

U-Value (BTU/(h·ft²·°F)

0.081

0.051

Table 27: Glazing Baseline Changes eQUEST Default

ASHRAE Standard

Type

Double Glazing Clear Pane

Double low-e glazing with paint

SHGC

0.604

0.25

U-Value (BTU/(h·ft²·°F)

0.536

0.5

Table 28: Internal Loads Baseline Changes

Lighting Power Density

eQUEST Default

ASHRAE Standard

0.9 W/ft2

1.02 W/ft2

Equipment Power Density

1.340 W/ft2

1 W/ft2

Occupancy

100 ft2 /person

275 ft2/person

5. Parametric Simulation From the baseline, only one parameter is changed for each run. For each of the parameters, there are several changes that aim to model realistic options but also aim to decrease overall EUI of the building. From the different options for each parameter, the option with the best EUI is chosen as the new base case for the next parameter change until all the parameters have been modeled. The case with the best EUI is chosen as the best case of the overall building.

â&#x20AC;˘ Parametric Analysis for Envelope Thermal Properties The building envelope parameters, namely the external wall, roof, slab and glazing, are modified to understand their impact on the overall building energy use. The parametric analysis for these different properties is conducted in the order wall-roof-slab-glazing, where the best case out of multiple simulation runs obtained at every step is chosen and taken as the reference for the next set of analyses. The parametric analysis for building envelope revolves around its U-value, which is the measure of a materialâ&#x20AC;&#x2122;s effectiveness as an insulator.

5.1.1 External Wall Parametric Analysis The modified baseline model on eQUEST has an external wall U-value of 0.051 Btu/h-ft2-F, which is the maximum value acceptable by ASHRAE standards. We conduct 5 runs of simulations, reducing the U-value of the woodframe external wall structure after every simulation, to observe if the overall energy consumption reduces with the decreasing U-value. The approach of reducing the overall U-value is based on replacing existing layers with layers with better insulating properties, changing the thickness of the default layers to acceptable values, and adding new layers to the existing set. Since our external wall is of R-13 wood frame type, we use an average cavity depth of 0.293-0.450 feet or 3.5-5 inch, as recommended for such types. The summary of the 5 simulation runs conducted is given as follows: Table 29 Simulation cases for External Wall

S.No.

Case

U-Value

Electricity

Gas

Source EUI

(MWh)

(MBtu)

(kBtu/ft2year)

Percentage change from Baseline (%)

Baseline

0.291ft Wood Shingle+0.291 Wood sft ¾ in+ 0.100 ft Gypsum Build 5/8 in

0.051

502.29

411.75

141.4

0.291ft Wood Shingle+0.291 Wood sft ¾ in+ 0.293 ft Gypsum Build 5/8 in

0.046

501.29

391.58

140.6

-0.9

0.291ft Wood Shingle+0.291 Wood sft ¾ in+ 0.293 ft MinWool Batt R13

0.036

504.57

405.32

141.7

0.2

0.291ft MinBd 3in R10.4 +0.291 Wood sft ¾ in+ 0.293 ft MinWool Batt R13

0.028

504.96

392.73

141.5

0.07

0.450 ft MinBd 3in R10.4 +0.291 Wood sft ¾ in+ 0.293 ft MinWool Batt R13

0.024

505.14

385.83

141.4

0.450 ft MinBd 3in R10.4 +0.291 Wood sft ¾ in+ 0.293 ft MinWool Batt R13 + 0.450 ft Wood Shingle

0.020

502.48

354.48

140

-1

The results display the change in the building electricity use, gas consumption and the source Energy Use Intensity (EUI) with the decreasing U-value, and the best case gives a source EUI value of 140 kBtu/ft2-year. We observe that there is a general decrease in the site EUI value, as we reduce the U-value, which justifies and reaffirms our understanding that improving the insulation thickness and material of the external walls improves the EUI value. This can be further observed in the gas consumption reduction as we perform the step-wise simulation. This can be explained by the decreasing need for gas in space heating, which is a major end-use sector in a cold climate place like Pittsburgh. The end-use site energy consumption for the different wall cases is shown in Figure 10. From

the figure, we see that there is a decrease in the space heating requirement, which leads to an improvement in the model. Apart from that, the other sectors consume roughly the same amount of energy. This is why we do not observe a considerable drop in the EUI value.

Figure 30 Energy Consumption for different wall cases However, this decrease in the overall gas consumption does not translate into significant EUI reduction, as the electricity consumption in the building does improve by much. The assumption before the parametric analysis was that the reduction in gas consumption would compensate for the constant or increased electricity requirement, reducing the expected EUI value considerably. The obtained results, however, show that the electricity requirement increases with the decreasing U-value. This can be explained by the increased cooling requirement during summer period, due to higher insulation in building. It is only in the final case that the electricity use again drops to near the baseline value, making for an interesting curve. There is an exception to this trend, which is observed in case 2, where the energy use goes up, contrary to the general and expected trend. This unexpected trend can be explained due to the insulation layer of ‘MinWool R-13’ which leads to a sudden increase in the overall energy use. 5.1.2 Roof Parametric Analysis The modified baseline U-value is 0.032, the maximum accepted value as per the ASHRAE standards. We conduct a set of 4 simulations to decrease the U-value and study its effect on the building EUI. As mentioned in the report, this is one of the primary objectives of the analysis, to understand if there is a clear inverse relation between the U-value of building EUI. So, the approach to changing the U-value remains the same as that for external wall simulation. The default roof given is eQUEST is of ‘above the deck insulation’ type. Taking this as the reference, we make the further required modifications. The summary for the roof simulation is shown as follows: Table 30 Simulation cases for Roof

S.No.

Case

U-Value

Electricity (MWh)

Gas (MBtu)

Source EUI (kBtu/ft2year)

Baseline

Percentage change from Baseline (%)

0.785 Blt-up roof 3/8in+ 0.450 ft MinBd 3in+ 0.063 Wood soft 3/4in

0.032

502.48

354.48

140

0.785 Blt-up roof 3/8in+ 0.450 ft MinBd 3in+ 0.250ft Wood soft 3/4in

0.030

502.02

336.79

140.2

0.14

0.785 Blt-up roof 3/8in+ 0.450 ft MinBd 3in+ 0.250ft Min Wool Batt R13

0.026

504.53

353.68

141.4

0.785 Blt-up roof 3/8in+ 0.450 ft MinBd 3in+ 0.250ft Min Wool Batt R13+0.250 ft feet Wood Shingle

0.023

502.54

324.22

140

0.785 Blt-up roof 3/8in+ 0.450 ft MinBd 3in+ 0.250ft Min Wool Batt R13+0.450 ft feet Wood Shingle + 0.250 Soil contact

0.014

505.82

331.65

141.5

From the results obtained for roof, we observe that there is negligible decrease in the EUI best case when compared with the reference obtained from the external wall simulation. The best case is observed in 3, and the best-case source EUI value is 140 kBtu/ft2. We choose this value over the baseline, as it has a lower gas energy consumption of 324.2 MBtu. In the difference roof cases, emphasis was placed on reducing the U-value to observe its effect on the building EUI, based on the assumption that improved envelope insulation for a cold climate place such as Pittsburgh would decrease the overall energy consumption. As shown in the table, gas

consumption falls considerably as we decrease the U-value. The exception is case 4, where despite having a lower u-value, the energy consumption is higher than the best case i.e. case 3. The end-use consumption for different roof scenarios is displayed in Figure 11.

Figure 31 Energy Consumption for different roof cases The result patterns for roof are similar to results obtained for external wall. Again, the major inference that we can make from our obtained results, is that merely a reduced U-value does not imply an improved building energy use. 5.1.3 Slab Parametric Analysis For the third stage of the envelope properties parametric analysis, we modify the slab values and observe its effect on the building source EUI value. The default slab type is given as construction 29- uninsulated construction, which is obtained from the â&#x20AC;&#x2DC;underground floorâ&#x20AC;&#x2122; values. Modifications are made to the existing values, and new insulation layers are added through 4 simulation runs, to bring down the U-Value and study its effect on the building EUI, an approach similar to that for wall and roof. The summary of simulation results for slab are shown as follows: Table 31 Simulation cases for Slab S.No. Case U-Value

Electricity

Gas

(kWh x 1000)

Source EUI (kBtu/ft2year)

Baseline

0.670 ft Soil 8in + 0.667 ft Conc HW 140 lb 8in

0.029

502.54

324.22

140

Percentage change from Baseline (%)

0.450 ft Soil 8in + 0.450 ft Conc HW 140 lb 8in + 0.450 ft Blt-up roof 3/8 in

0.027

502.35

314.18

140

0.250 ft Soil 8in + 0.450 ft Conc HW 140 lb 8in + 0.450 ft Wood Sft 3/4in

0.025

502.15

317.41

140.3

0.2

0.250 ft Soil 8in +0.450 ft Conc HW 140 lb 8in + 0.450 ft MinWool Batt R13

0.023

503.88

330.35

140.9

0.6

0.250 ft Soil 8in +0.450 ft Conc HW 140 lb 8in + 0.450 ft Soil Contact for uninsulated

0.016

505.58

323.58

141.2

0.8

Figure 32 Energy Consumption for different slab cases The end-use consumption for the different slab scenarios is shown in Figure 12. We see that there is very slight decrease in the space heating consumption from baseline to case 1, and increases again in the remaining cases. From the above results, we see that the gas energy consumption falls further by 10 MBtu and the overall EUI value remains the same. Case 1 gives us the best case with an EUI value of 140 kBtu/ft2. Again, this case is chosen over the baseline scenario due its lower gas energy consumption. This observation is similar to that obtained for the performance analysis conducted on Revit, where slab modifications had no positive impact on the overall building energy use. The advantage of eQUEST over Revit in this case is that the former allows us to compute the change in gas energy consumption for every case. Unlike wall and roof, the results for slab are not straightforward, as there is no direct reduction observed in the gas energy consumption with a decreasing U-value. Thus, using case 1 gives us the best-case scenario, which integrates the best results for the external wall, roof and slab. We use this model to proceed with the internal load parametric analysis. 5.1.4 Glazing Parametric Analysis Pittsburgh is inside the climate zone 4A with the maximum U value of 0.35 for the baseline based on ASHRAE 90.1 2016. Low emissivity coating is recommended for cold climate dominated weather like Pittsburgh. Therefore, low-e coating is applied for every glazing selections. For updated baseline glazing, we selected double low-e tinted glass with 0.5-inch Argon gap. For the parametric analysis, better performance glazing assemblies are chosen to investigate the impact of U-value, SHGC and Visible Transmittance on energy Performance. The selected glazing assemblies are shown in the Table 11. The index of each glazing type is extracted from DOE-2 glass library. Table 32 Specification of Glazing Selections Visible Glazing Type Gap Gas Fill U-value SHGC Transmittance Baseline Double Low-E (e2=.04) Tint

0.5-inch Argon

0.33

0.28

0.41

Type 1

Triple Low-E Film (66) Clear

0.5-inch Air

0.37

0.36

0.54

Type 2

Triple Low-E Film (66) Tint

0.5-inch Air

0.37

0.25

0.32

Type 3

Triple Low-E Film (33) Tint

0.5-inch Air

0.35

0.15

0.17

Type 4

Quadruple, Two Low-E Glass, Two Low-E Film, Clear

0.31-inch Krypton

0.2

0.45

0.62

As U-value and SHGC decreases, energy use intensity shall also decrease theoretically. And the energy simulation output shown in Figure 13 from eQUEST verified this.

Figure 33 Comparative Analysis of Glazing Types in Site Energy From Figure 13, end-use data is shown for every glazing type. There is a slight decrease when U-value and SHGC drops down from Type 1 to Type 3 and a slight increase when SHGC increases from Type 3 to Type 4 while Uvalue decreases. Figure 1 showed that the energy consumption could be affected by the overall performance of glazing from U-value to SHGC. Selection of glazing shall base on U-value, SHGC and visible transmittance.

Figure 34 Monthly Electric Consumption Analysis for Glazing Selections Figure 14 shows that monthly electric consumption drops when SHGC decreases. And this can be a linear relationship between SHGC and electric consumption. From type 1 to Type 3, SHGC decreases while Type 4 has the highest SHGC and Baseline has the second highest SHGC. Lower solar heat gain coefficient indicates that

window area has fewer solar heat gain throughout the year. For summer, lower SHGC is preferred while Higher SHGC is preferred for winter. Hence Type 4 has lower gas consumption for heating in winter shown in Figure 15.

Figure 35 Monthly Gas Consumption Analysis for Glazing Selections Figure 15 shows that type 4 has the lowest gas consumption for every month due to its relatively lowest U-value of 0.2. But generally, gas consumption does not have obvious relationship with U-value or SHGC. The amount of gas consumption depends on total performance of glazing types. Table 33 Energy Performance Comparison Glazing Parametric Electrical Gas Consumption Simulation Consumption (kWh (MBtu) x 103)

Source EUI (KBtu/ft2/yr)

Percentage Change(EUI)

Baseline

502.35

312.25

140.05

Type1

510.59 (+1.64%)

308.24 (-1.28%)

142.12

+ 1.47%

Type2

498.13 (-0.84%)

324.03 (+3.77%)

139.25

- 0.57%

Type3

490.03 (-2.45%)

343.38 (+9.97%)

137.62

- 1.74%

Type4

530.07 (+5.52%)

234.37 (+24.94%)

145.34

+ 3.78%

And Table 12 showed that Type 3 has the best energy reduction of 1.74% based on baseline model. Although Type 4 (Quadruple, Two Low-E Glass, Two Low-E Film, Clear) glazing possesses lowest U-value, but its SHGC is higher than other types. Type 4 can be regarded a very insulated glazing that lead to a significant heating load reduction and saving on gas consumption. However, a small increase due to its good insulation effect. In summer, heat emission to outdoor during night time may be difficult and the heat may be accumulated to cooling load for the next daytime. In winter, it would be perfect to use quadruple pane due to its superior ability

of heat preservation. Pittsburgh tends to have a hot summer and Triple low-e film tint glazing performs better for such weather context. 5.2 Internal Loads 5.2.1 Parametric Analysis of Occupant Density (OD) The default value of occupant density taken by eQUEST is 100 ft2 per person. And eQUEST assigned the occupants based on floor area of each zone. For the parametric analysis, we follow the ASHRAE 62.1 2016 (Ventilation for Acceptable Indoor Air Quality) Standard [1] based on the zone types. For MRQ building, it is a combined usage of Education and Office. There are many classrooms and labs according to AutoCAD floor plan. Based on given floor plan and thermal zoning, we recreated floor plan as shown in Figure 16, 17 & 18.

Figure 16 1st Floor Plan

Figure 17 Second Floor Plan

Figure 18 Third Floor Plan

Table 34 ASHRAE 62.1-2016 Standard for OD based on Occupancy Category Baseline Design Proposed Design 2 2 Occupancy Category OD (ft /person) OD (ft /person) Source Office Space 200 ASHRAE 62.1-2016 Classroom (age 9+) 29 ASHRAE 62.1-2016 Lab & Computer Lab 40 ASHRAE 62.1-2016 Cafeteria 50 ASHRAE 62.1-2016 100 for all thermal zones Art Classroom 50 ASHRAE 62.1-2016 Reception areas 33.33 ASHRAE 62.1-2016 Entry Lobby 100 ASHRAE 62.1-2016 Corridor â&#x20AC;&#x201D; ASHRAE 62.1-2016 Table 13 shows the occupancy density taken from ASHRAE 62.1-2016 standard for the parametric analysis.

Figure 36 End Use Energy Consumption Comparison Figure 19 shows that changing of occupancy density significantly affects heating load. The reason is that total amount of people for the proposed design. Hence heating load rises in winter since Pittsburgh is a cold climate dominated city with large indoor and outdoor temperature difference. Significant rise of gas consumption shown in Table 14 also verified this explanation. Natural gas is the main source of heating for the building in winter. Table 35 Energy Performance Comparison of Baseline and Proposed OD design Electricity Source EUI Occupancy Density (kWh x 10^3) Gas (MMBtu) (KBtu/ft2/yr) Baseline 530.07 234.37 145.34 Proposed Design 544.24 (+2.7%) 766.96 (+227%) 162.74 (+12%) Although default occupancy density baseline consumes less energy, proposed detailed design are chosen for further parametric analysis. The reason is that occupancy density design should base on detailed usage type of every zone. 5.2.2 Lighting Power Density Lighting Power Density (LPD) is measured by W/ft2 and the ASHRAE 90.1-2012 standard says that the minimum required LPD for a medium sized office is 1.02 W/ft2. eQUEST had a default value of 0.9 W/ft2, which did not meet the minimum standard. Thus, the new baseline had 1.02 W/ft2 as the LPD. ASHRAE 90.1-2007 Table 9.6.1 breaks down the LPD per room usage type. For open and enclosed office type spaces, the minimum LPD should be 1.1 W/ft2, for conference, meeting rooms, or multipurpose spaces, the minimum LPD should be 1.3. Thus, for the next two parametric runs, the overall LPD of the building was set to 1.1 and 1.3 W/ft2 as if the whole building had office and conference type lighting. As expected, the total source energy increased from the base LPD run, which was the best case from occupancy schedule. More electricity is

needed to provide more wattage per square foot, which increases total source energy and electricity usage. Total source energy is divided by the area of the building to get a total source EUI in KBtu/ft2/yr. However, the more lighting there is, the less the heating load will become since more lights give off more heat. However, this small source of heat gain is not enough to offset the extra electricity that is needed, thus leading to an increase in total source EUI. The final parametric run varied the LPD across thermal zones. Looking at the given AutoCAD files that gave the layout and room usage of the entire building, a single usage type was assigned to each thermal zone on each floor. Depending on the usage type, the LPD changes based on ASHRAE 90.1-2007 Table 9.6.1. For MRQ specifically, most of the rooms were offices, classroom, labs or corridors. Table 15 shows the LPD for each specific type. A breakdown of each thermal zone by usage type was previously given in Section 1.1. Table 36: Room Usage Type LPD (ASHRAE 90.1 Table 9.6.1) Room Usage Type

LPD (W/ft2)

Office

1.1

Classroom

1.3

Cafeteria

1.2

Restroom

0.9

The different LPD had a higher total source EUI than 1.1 W/ft2, which was overall office, but a smaller EUI than 1.3 W/ft2, which was overall conference space, indicating the average was between the two LPD. However, the ASHRAE standard of 1.02 W/ft2 had the lowest total source EUI, so it was chosen as the baseline for the next parameter analysis. In reality, the varied LPD based on usage type would be more representative of a building. However, for the sake of parametric analysis of a model, the baseline of the ASHRAE standard of 1.02 W/ft2 was chosen. Compared to the new baseline model, the best case of the LPD parametric runs increased from 145.6 kBtu/ft2/yr to 162.7 kBtu/ft2/yr. This represents a 11.7% increase. The electricity, fuel, and total source energy usages for each parametric run can be seen in the table 16. Table 37: LPD Parametric Analysis Results LPD (W/ft2) Electricity (kWh x000) Base (1.02) 544.24 1.1 555.46 1.3 583.87 Varies by thermal zone 570.56

Fuel (Btu x000,000) 766.96 755.38 727.79 737.61

Total Source EUI (KBtu/ft2/yr) 162.7 165.4 172.1 168.9

As the LPD increases, we can see in the figure below that the space cooling is increasing marginally. However, the end use breakdown remains approximately the same as the EUI increases a small amount. There is a small

increase in lights, space cooling, pumps & auxiliary and a small decrease in space heating across the parametric runs.

Figure 37: Lighting Power Density End Use Breakdown 5.2.3 Equipment Power Density Equipment Power Density (EPD) represents the energy usage from plug loads, like computers, desk lights, or printers. It is measured as an average W/ft2 across the building. eQUEST had a default EPD of 1.34 W/ft2, which was changed to 1 W/ft2 based on ASHRAE 90.1. In a report published by NREL, written in 2014, there are several plug and process load densities reported in literature [2]. In an article by Metzger written in 2011, the average EPD for an office was cited to be 0.9 W/ft2. This value was used as the EPD for the first parametric run. ASHRAE 2012 Appendix 4A cited 0.75 W/ft2 as an appropriate EPD for offices. This value was used as the EPD for the second parametric run. Finally, Wilkins and Hosni cite a range of 0.25 to 2.0 W/ft2 [3]. 0.25 W/ft2 was used as the EPD for the third and final parametric run. This value is very aggressive and assumes that the entire office uses 100% notebooks, have one printer per ten employees, speakers and miscellaneous items. The equipment used is also new and thus energy efficient, which is not an unreasonable assumption. Even though it is very aggressive to have an EPD for 0.25 W/ft2, Wilkins and Hosni have shown it is possible and has been done. Thus, for the sake of the parametric analysis, this case was chosen as the best case within EPD changes. As expected, the lower the EPD, the lower the total source energy use. As electricity usage decreased, the fuel usage increased. Much of the equipment used in offices emit a large amount of heat waste, which would be drastically reduced in the 0.25 W/ft2 EPD case. Table 38: EPD Parametric Analysis Results EPD (W/ft2) Electricity (kWh x000) Fuel (Btu x000,000) Total Source EUI (kBtu/ft2/yr) 1.0 544.24 766.96 162.7 0.9 521.70 797.61 157.6 0.75 487.96 853.23 150.1 0.25 380.74 1066.70 127.4

The final total source EUI for the 0.25 W/ft2 was 127.4 kBtu/ft2/yr, which represents a 21.68% decrease in total source energy. The electricity usage decreased by 30.04% and the fuel usage increased by 39.08%. Even though fuel usage increased by a higher percentage than electricity, the total source energy was still lower. This might be because the electricity is coming from an unknown power plant. Since power plants have low efficiencies and transmission losses, electricity savings have a higher weight in the total source energy than adding some fuel, which is mostly natural gas burned on site for heating. As the EPD decreases, we can see a decrease in total usage. The majority of this decrease comes from the miscellaneous equipment. However, there is also a large increase in space heating that comes with the loss of miscellaneous equipment. Without the extra equipment, all the heat that emanates from them are gone, which adds to the heating load, as seen in the end use breakdown below.

Figure 38: Electric Power Density End Use Breakdown 5.3 Scheduling eQUEST enables designers to modify occupancy schedule, lighting schedule, equipment schedule, HVAC schedule and so on. In the parametric analysis, we modified occupancy, lighting and equipment schedule based on ASHRAE 90.1 2004 user manual and some scholarâ&#x20AC;&#x2122;s research. 5.3.1 Occupancy Schedule The new baseline follows use manual of ASHRAE 90.1-2004 and we proposed a new occupancy schedule with a slight change of peak occupancy load shown in Figure 8 and 9. The peak occupancy load from 10-12 pm is changed from 0.95 to 0.97 and 0.98 respectively since people tend to increase at noon time in office buildings. And for afternoon time, occupancy load tends to increase from 2-4 pm since people may come back from lunch hour gradually. For 6-7pm, people tends to finish their work and leave the office building and it is a gradual change. The proposed occupancy schedule addresses on gradual change of occupancy load in the building shown in the following 2 figures.

Figure 39 Occupancy Schedule based on user manual of ASHRAE 90.1-2004

Figure 40 Proposed Occupancy Schedule Table 18 shows that proposed design does not impact so much on energy performance. And it is feasible to conduct such schedules. But I think occupancy schedule should be fix for energy modeling since people are moveable and flexible. Fixed schedule may cause a large performance gap between operation and design stage. So, it is important to integrate dynamic thermal interaction of occupants with surrounding environment into eQUEST to enhance the building simulation effectiveness. Table 39 Energy Performance Comparison of baseline and proposed occupancy schedule Electricity Consumption (kWh x Gas Source EUI 3 Occupancy Schedule 10 ) Consumption(MMBtu) (KBtu/ft2/yr) Baseline 544.24 766.96 162.74

Proposed Design

544.89(+0.12%)

763.38 (+0.5%)

162.82 (+0.05%)

Baseline model based on user manual of ASHRAE 90.1-2004 is chosen for further parametric analysis due to its lower source EUI. 5.3.2 Equipment Schedule Once the equipment power density was determined, another factor that plays into the energy consumption of a building is the schedule on which the equipment is operated. The equipment usage corresponds directly to the occupancy, as each person has their own set of equipment plug loads. The schedule is operated in the same way as the lighting schedules, with percentages of use per hour, differentiated between weekdays, Saturday, and Sunday. After the eQUEST defaults were brought up to ASHRAE standards, one parametric change was made, as seen in the table below. The changes were reflective of the occupancy schedule change, which increased the percentage during normal business hours on the weekday. Table 40: Changes on Weekday Equipment Power Time From To 9-10am 0.9 0.95 10-11am 0.9 0.97 11-noon 0.9 0.98 1-2pm 0.9 0.95 2-3pm 0.9 0.98 3-4pm 0.9 0.98 4-5pm 0.9 0.95 The original and modified equipment power schedule can be seen in Figure 24.

Figure 41: Equipment Power Schedule Usage Percentage The resulting total source energy use increased, as was expected. A higher percentage of equipment usage would consume more electricity while lowering the heating load. The electricity usage was 381.89 MWh and the

fuel usage was 1062.2 MMBtu. This represents a 1.15% increase in electricity usage and a 0.42% decrease in fuel usage. The total source EUI of the new schedule was 127.6 kBtu/ft2/yr, which represents a 0.15% increase EUI. Similar to the lighting schedule, it is very difficult to adjust equipment usage schedules. On a parametric analysis basis, the baseline schedule brought up to ASHRAE standards generate a better energy usage for the building. If a large portion of the equipment were not used, there would be significant energy savings, but that is not realistic. Therefore, the second parametric model for equipment schedules increased the percentages rather than decreased, even knowing that it would lead to a higher total source energy usage. 5.3.3 Lighting Schedule Once the lighting power density was determined, another factor that plays into the energy consumption of a building is the lighting schedule. The lighting schedule of a building is often set automatically, so that every weekday would be exactly the same, regardless of how many people are actually in the building. The schedule extends to Saturday and Sunday, which have their own respective schedules. eQUEST models lighting schedule based on the percentage of lights that are on during the span of one hour. Each hour has a fraction indicating the percentage of lights that are on. After the eQUEST defaults were brought up to ASHRAE standards, one parametric change was made, as seen in the table below. The changes were reflective of the occupancy schedule change, which increased the percentage during normal business hours on the weekday. Table 41: Changes on weekday for Lighting Schedule Time From To 9-10am 0.9 0.95 10-11am 0.9 0.97 11-noon 0.9 0.98 1-2pm 0.9 0.95 2-3pm 0.9 0.98 3-4pm 0.9 0.98 4-5pm 0.9 0.95 The baseline and new lighting schedules can be seen in the figure 25.

Figure 42: Lighting Schedule Usage Percentage

The resulting total source energy use increased, as was expected. A higher percentage of lights that are on during the day would consume more electricity while lowering the heating load marginally. The electricity usage was 550.38 MWh and the fuel usage was 762.21 MMBtu. This represents a 1.13% increase in electricity usage and a 0.6% decrease in fuel usage. The total source EUI of the new schedule was 164.2 kBtu/ft2/yr, which represents a 0.9% increase energy usage intensity. On a parametric analysis basis, the baseline schedule brought up to ASHRAE standards generate a better energy usage for the building. However, even if the schedule could be modified to generate lower energy usages for the building, it is very difficult to adjust lighting schedules in real life. If a large portion of the lights were turned off all the time, there would be significant energy savings, but that is not realistic. This is why the second parametric model for lighting schedules increased the percentages rather than decreased, even knowing that it would lead to a higher total source energy usage. 5.4 Daylighting (BONUS) It is very important to integrate daylighting sensor with artificial lighting control in order to reduce electricity consumption and further enhance indoor environment comfort. In the parametric analysis, two daylighting sensors have been put into different locations shown in Table 21 to detect daylighting level and control artificial lighting lux level. Table 42 Energy Performance Comparison of Daylighting Parametric Analysis Electricity Gas 3 Daylighting Analysis (kWh x 10 ) (MMBtu) Baseline (no daylighting) 380.74 1066.7 Scenario 1 Sensor 1: 3m away from external wall 340.98 1088.8 Sensor 2: 6m away from external wall (-10.44%) (+2.07%) Sensor Height: human chest level (3.94 ft) Scenario 2 Sensor 1: 3m away from external wall 340.97 1088.9 Sensor 2: 6m away from external wall (-10.44%) (+2.07%) Sensor Height: desk level (2.5 ft) Scenario 3 Sensor 1: 2m away from external wall 341.02 1088.8 Sensor 2: 4m away from external wall (-10.43%) (+2.07%) Sensor Height: desk level (2.5 ft)

Source EUI (KBtu/ft2/yr) 127.46

118.67 (-6.9%)

118.68 (-6.9%)

As shown in Table 21, Total source EUI decreases by 6.9% when daylighting control is integrated. Changing location of sensors has no impact on energy performance since the change does not affect the detection of natural light level through glazing area. But putting sensors at desk level is more reasonable since people normally will look at the paper and stuff on the desk. And Scenario 2 consumes less energy compared to scenario 3 so that scenario 2 is selected to be the overall best case.

Figure 43 End Use Comparison of Daylighting Analysis Figure 26 displays the end use intensity for every scenario, it is obvious that lighting consumption drops when integrated daylighting control. And other types of usage do not change that much. However, gas consumption increases shown in the Table 21 since lighting reject less heat in winter and heating load increases. 6. Baseline versus Best Design Model The final best case is generated from the best performing (based on Annual Source EUI) parameters from every stage of parametric design including building envelope, internal loads, scheduling and daylighting control. The table shown below displays all the parameters. Table 43 Input Parameters Comparison of Baseline & Best Design Model Parameters Baseline Best Design Model Envelope 0.450 ft MinBd 3inch 0.291ft Wood Shingle+0.291 R-10.4 +0.291 Wood sft ¾ in+ 0.293 External Wall - Construction Wood sft ¾ in+ 0.100 ft ft MinWool Batt R13 + 0.450 ft Wood Gypsum Build 5/8 inch Shingle External Wall - U-value 0.051 0.020 0.785 Blt-up roof 3/8in+ 0.785 Blt-up roof 3/8in+ 0.450 ft Roof - Construction 0.450 ft MinBd 3in+ 0.063 MinBd 3in+ 0.250ft Min Wool Batt Wood soft 3/4in R13+0.250 ft feet Wood Shingle Roof - U-value 0.032 0.023 0.670 ft Soil 8in + 0.667 ft 0.450 ft Soil 8in + 0.450 ft Conc HW Slab - Construction Conc HW 140 lb 8in 140 lb 8in + 0.450 ft Blt-up roof 3/8 in Slab – U-value 0.029 0.027 Double Low-E (e2=.04) Tint Triple Low-E Tint Glazing - Type 0.5-in Argon filled 0.5-in Air filled

Glazing - U-value Glazing - SHGC Glazing - Visible Transmittance Internal Loads 2

0.33 0.28 0.41

Occupancy Density (ft /p)

100 for all thermal zones

Lighting Power Density (W/ft2)

0.9 for all thermal zones

Equipment Power Density (W/ft2) Daylighting

Energy Performance Annual Electricity Consumption Annual Gas Consumption Total Annual Source EUI

0.35 0.15 0.17 Office-200, classrooms-29, Lab-40, cafeteria -50, Entry Lobby-100, Art class-50, Reception-33 Office-1.1, Class-1.3, Cafeteria-1.2, Restroom-0.9 (ASHRAE 90.1-2007)

1.34 for all thermal zones

0.25 for all thermal zones

No daylighting control

Daylighting control Sensor 1: 3m away from external wall Sensor 2: 6m away from external wall Sensor Height: desk level (2.5 ft)

1724.50 MMBtu 493.21 MMBtu 145.6 KBtu/ft2/yr

1162.54 MMBtu (-32.59%) 1088.9 MMBtu (+120.78%) 118.68 KBtu/ft2/yr (-18.49%)

As shown in Table 22, the major changes are driven by reducing the U-value, especially for non-window faรงade design. The approach of reducing the overall U-value is based on replacing existing layers with better insulated properties, changing the thickness of the default layers to acceptable values, and to add new layer to the existing set. It is observed that there is a general decrease in EUI value as we reduce the U-value. The best model from external wall is the assembly with lowest U-value. However, the best energy performance roof model is the assembly with second lowest U-value. The reason is that better insulation may prevent the heat emission from indoor to outdoor in summer. The best slab model with second highest U-value verifies that higher U-value does not mean better energy performance. For glazing assembly selection, based on overall judgement of U-value, SHGC and visible transmittance, we selected four assemblies with Low-e coating to investigate its impact on energy performance. And the triple Glazing Low-E Tint glazing assembly outweighs other assemblies due to its lowest SHGC and relative lower Uvalue. The internal loads design, consisting of occupancy density, lighting power density, and equipment density, follows ASHRAE 62.1-2016 and ASHRAE 90.1-2012 standard and some research papers. Occupancy density and lighting power density is assigned to each thermal zone according to a recreated detailed floor plan based on floor layout and room usage of the entire building as well as. Each occupancy categories have their own density according to ASHRAE 62.1-2016. Best Design is chosen by comparing the energy performance and serious consideration of feasibility. The end-use energy of New Bassline model and Best design model are shown in the Figure 27. Best design model has much higher equipment usage and around 280% higher heating usage and slightly lower cooling

usage based on new baseline. Because equipment power density drops from 1.0W/ft2 to 0.25 W/ft2. And the reduction of heat rejected from equipment also increases the heating load.

Figure 44 End-use Energy Comparison between New baseline and Best design model Figure 28 shows the total energy performance in aspects of electrical consumption and gas consumption as well as total source EUI. Although gas consumption increased by 121%, electricity consumption decreases by 32%. Meanwhile, the total annual source EUI decreases by 18.5%. Electricity turns out to be the most effective type of energy to be reduced in buildings since electricity consumes more primary source energy.

Figure 45 Energy Consumption Comparison between New Baseline & Best Design Model

7. Comparison of Best Design Model with US Benchmark From our results, we observe that there is a clear improvement in the building energy use compared to its baseline ASHRAE equivalent, achieving a reduction of 18.49%. To understand the performance in comparison to the US benchmark for offices, the Energy Star Portfolio manager is used, which defines the EUI benchmark by the property type (Energy Star, 2016). Since the given model involves a medium-sized office building, the source EUI value for ‘office’ type is used, which is 148.1 kBtu/ft2. We observe that the EUI value for our best-case scenario is 118.68 kBtu/ft2, which is a reduction of 19.8% from the benchmark value. It is important to notice that even the baseline model seemed to meet the benchmark set by Energy Star, having an EUI value 1.6% lesser than the US benchmark EUI for office buildings. By conducting the parametric analysis and choosing the optimum building properties for the Pittsburgh climate and office type, the building performance is further improved, as evident in the best-case result. We have taken the source EUI over site EUI from the Energy Star report to compare our results, as eQUEST provides us with primary energy (gas) and secondary energy (electricity) results. Since we have both results, source energy combines these two into a single unit, for comparison purposes. Conclusion •

A detailed energy performance analysis was conducted for MRQ office building was conducted using eQUEST and the developed model and results were compared with those obtained in Revit

•

The eQUEST and Revit baseline model default assumptions were compared and their individual pros and cons were discussed

•

The baseline eQUEST model was further modified to meet the 2016 ASHRAE 90.1 requirements, by changing the required parameters

•

The Air Handling Units (AHU) were redesigned for one per floor from one per thermal unit as the default AHU setup was unnecessary

•

Parametric analysis was conducted in different stages to develop the best-case scenario: Building envelope (wall, roof, slab, glazing), Internal Load (lighting power density, equipment power density and occupation density), office scheduling- occupancy schedule, equipment schedule and lighting schedule

•

Decreasing the U-value had a positive effect on EUI in case of wall, but did not directly improve the EUI during roof and slab simulation runs

•

The building envelope had negligible impact of the overall EUI, only decreasing the gas energy consumption of the building by 23%

•

The lowest lighting power density (1.02 W/ft2) gave the lowest overall EUI. Increasing LPD resulted in increased electricity consumption and slightly decreased fuel consumption.

•

The lowest equipment power density of 0.25 W/ft2 gave the lowest overall EUI. This change from the ASHRAE 90.1 baseline of 1 W/ft2 decreased overall EUI by 21.68%.

Bibliography [1] Energy Star. (2016). U.S. Energy Use Intensity by Property Type . Technical Reference. Energy Star. Retrieved from https://portfoliomanager.energystar.gov/pdf/reference/US%20National%20Median%20Table.pdf [2] Office of Energy Efficiency & Renewable Energy, "Guides and Case Studies for All Climates," [Online]. Available: https://energy.gov/eere/buildings/guides-and-case-studies-all-climates. [Accessed 18 September 2017]. [3] International Code Council, "Appendix D: Degree Day and Design Temperatures," International Code Council, 2006. [4] M. H. Christopher Wilkins, "Plug Load Design Factors," ASHRAE Journal, May 2011.

48-524/722 BUILDING PERFORMANCE MODELING

Assignment 3 DesignBuilder Energy Performance Analysis

Group 2: Xinxin Hu Amber Jiang Akhil Mathur

Abstract This project is a further study of detailed building envelope and HVAC system design in terms of building energy performance. The case study building is MRQ building, a 3-storey office/educational building located in Pittsburgh, Pennsylvania. The energy performance analysis was implemented using DesignBuilder, with has EnergyPlus embedded. HVAC system variation is the highlight for the report. Two different HVAC systems were investigated; one is air-cooled chiller with constant air volume (CAV) air distribution system, the other is water-cooled chiller with variable air volume (VAV) air distribution system. Along with the air-cooled chiller and CAV air system, different envelope assembly was investigated based on the understanding of the previous two projects using eQUEST and Revit. The baseline envelope design follows ASHRAE 90.1-2010. And the proposed envelope was from the best envelope we got from previous two project using parametric design. At last, we also installed solar PV panel on roof and overhangs at 3rd floor. Four models were created using the baseline HVAC and baseline envelope and they were simulated to generate annual energy consumption of the building along with monthly, seasonal and peak energy consumption. The result indicated the VAV air distribution system with water-cooled system is more energy efficient since it does not need cooling and heating simultaneously comparing to CAV with air-cooled chiller system. The proposed VAV system achieved 30% reduction of site EUI and 40% reduction of source EUI. The proposed envelope also turns out to be better with 5.78% reduction of site EUI and 7.2% reduction of source EUI. The proposed VAV system with PV on the roof and as shading devices achieved 36% reduction of site EUI and 34.5% reduction of source EUI.

Table of Contents Abstract ......................................................................................................................................... 79 1. Introduction ............................................................................................................................... 81 2. Building Information ................................................................................................................. 82 2.1 Building Geometry ............................................................................................................................ 82 2.2. Climate Analysis .............................................................................................................................. 83

3.1. Baseline Conditions ................................................................................................................. 86 3.1.1. Thermal Zoning ............................................................................................................................ 86 3.1.2. Occupancy .................................................................................................................................... 87 3.1.3. Internal Load Density & Schedule ................................................................................................ 88 3.1.3.1. Interior Lighting ................................................................................................................................ 88 3.1.3.2. Interior Equipment ............................................................................................................................ 89 3.1.4. Thermostat Set Point ..................................................................................................................... 90 3.1.5. Flow Rate Setting ................................................................................................................................. 90 3.2. Baseline Building Envelope ............................................................................................................... 92 3.3 Baseline HVAC System ....................................................................................................................... 93

4. Alternative Envelope Model Description ...................................................................................... 97 4.1 Proposed Envelope Design ................................................................................................................. 97 4.2. HVAC System Design Alternative ................................................................................................... 98

5. Simulation Result Comparisons ................................................................................................. 103 5.1 Baseline Model vs Proposed Envelope Model ................................................................................... 103 5.2 [CAV & Air-cooled chiller] vs [VAV & Water-cooled chiller] ........................................................ 109 5.3. Baseline Building vs Final Modified Building ................................................................................ 114 5.4 Benchmarking Comparison............................................................................................................. 116

6. Proposed VAV vs Proposed VAV with PV system ....................................................................... 117 7. Conclusion ................................................................................................................................ 119

1. Introduction A detailed energy performance analysis using Energy Plus is conducted for a 3-storey medium-sized office MRQ building with a floor area of approximately 3,600 m2. This is a further and extensive study from the previous study where we used eQUEST and Revit to analyze the impact of building envelope properties, building operational schedules, as well as various types of internal loads on the energy consumption. From the previous study, we obtained a fundamental understanding of how these building criteria influence the building energy performance. In this study, a whole-building integrated energy simulation software, Design Builder, was used to analyze the effect of detailed building enclosure and HVAC (heating, ventilation, air-conditioning) on building energy consumption. The embedded engine, Energy Plus, is a sophisticated, powerful and widespread energy simulation software developed by U.S. Department of Energy. The raw data generated from the simulation process of EnergyPlus can be obtained and post-processed with various of intention. For MRQ Building, a 3-storey medium size office/educational mixed-use building, located Pittsburgh, a detailed energy model is developed using DesignBuilder. The input energy model parameters in DesignBuilder follows ASHRAE 90.1.2010 standard. The thermal zones are created according to architecture layout, as explained in the following sections. Comparison analysis is then conducted for the model based on EnergyPlus by modifying the thermal envelope properties (external wall, roof slab, glazing), HVAC system for the building. For the HVAC system air loop design, constant air volume(CAV) system and variable air volume(VAV) system were investigated. This is done in the given order to study the impact of each parameter on the overall building consumption, and consequently identify the key areas of improvement in the building. All parameters and obtained results are given in SI units. In the following part, key objectives of the study are listed. Key Objectives 8. To further study the interactive effect of building enclosure design on building energy performance 9. To study fundamental theory of different HVAC system application including CAV and VAV system 10. To conduct comparison analysis for the building thermal envelope properties using EnergyPlus and DesignBuilder to understand any potential correlation between the envelope U-value and overall energy consumption of the building 11. To conduct comparison analysis of CAV and VAV system application in buildings by comparing their annual, monthly energy performance variation 12. To study the solar PV panel electricity generation and interactive effect on energy performance on building

2. Building Information In this section, the basic information of the MRQ building will be described in terms of building geometry description, building site analysis and climate analysis. Other settings for the baseline and alternative building cases are introduced in the later sections.

2.1 Building Geometry MRQ building is a 3-storey office/educational mixed-use building located in Pittsburgh, Pennsylvania. The building surrounding building is the purple blocks. The two adjacent buildings were simulated since they blocked some solar radiation of MRQ building. The model built in the design builder is shown in Figure 1. The building has its longer side facing South and North. (0Â°: North) Total floor area of the building is 3422m2 for which unconditioned area of 437 m2 (toilet & mechanical room) accounts.

Figure 46 MRQ building

2.2. Climate Analysis Pittsburgh is considered a cold climate, meaning buildings need to have the capacity to heat during the winter 2. Using Climate Consultant, Figure 2 was generated. Pittsburgh experiences temperatures below freezing and temperatures as high as 100 F. These temperatures would require an office building to have heating and cooling capacities. Figure 1 shows the average dry bulb temperatures per month in Pittsburgh, PA.

Figure 47: Average Dry Bulb Temperature in Pittsburgh, PA

Figure 3 shows the average relative humidities for each month with a peak humidity in September and a low humidity in April. With high humidities, HVAC systems will need to ensure enough moisture is taken out of the air to avoid mold growth and keep the comfort of occupants at an acceptable level.

Figure 48: Average Relative Humidity in Pittsburgh, PA

Figure 4 is taken from Climate Consultant and shows the directionality of wind in the form of a wind rose. The max wind speeds reach 30 mph, mostly from the west direction, and minimum wind speeds of 6 mph from the northeast direction. The directionality of wind impacts energy efficiency of a building based on its orientation. If the side

M. Sheppy, "An Analysis of Plug Load Capacities and Power Requirements in Commercial Buildings," in 2014 ACEEE Summer Study on Energy.

with a large surface area is facing the side with higher wind speeds, there will be more infiltration, which will drive up heating and cooling costs.

Figure 49: Wind Rose in Pittsburgh, PA (Climate Consultant)

Figure 5 indicates the average wind speeds for each month, taken from climate consultant. Wind speeds are higher in the winter months, which impacts the heating loads because of infiltration through the envelope. Especially if the building is not optimally oriented, higher wind speeds in winter months could drastically increase the heating cost as well as cause drafts through the building, which negatively impacts occupancy comfort.

Figure 50: Average Wind Speed in Pittsburgh, PA

Figure 6 shows the average monthly solar radiation for Pittsburgh compared with Washington state and Nevada. The summer months have a higher radiation, with a maximum of approximately 5.4 kWh/m2/day. Increased solar radiation can increase cooling loads in the summer and can decrease heating loads in winter if the building is designed for passive heating and cooling. The winter months have a lower radiation, with a minimum of 1.9 kWh/m2/day. The yearly average is 4.15 kWh/m2/day. Washington state historically has lower solar radiation levels. Compared with Washington state, Pittsburghâ&#x20AC;&#x2122;s average solar radiation is 11% greater than the Washingtonâ&#x20AC;&#x2122;s average of 3.73 kWh/m2/day. Nevada historically has higher solar radiation levels, with an average of 6.6 kWh/m2/day. Compared with Nevada, Pittsburghâ&#x20AC;&#x2122;s average solar radiation is 37% lower3.

Figure 51: Monthly Solar Radiation Values for Pittsburgh, PA compared with highs and lows in US

M. Sheppy, "An Analysis of Plug Load Capacities and Power Requirements in Commercial Buildings," in 2014 ACEEE Summer Study on Energy.

3.1. Baseline Conditions In this section, the baseline conditions of the building models will be described. This includes the constant model inputs, which includes the thermal zoning divisions, the occupancy density and schedule, the internal loads and schedules, the thermostat set point, and the flow rate from the domestic hot water system.

3.1.1. Thermal Zoning Since the MRQ building is a mixed office/educational facility, different rooms have vastly different functions with different constant inputs. Thus, given the AutoCAD layout of each floor, the floors were divided into thermal zones based on room usage. The division of the thermal zones can be seen in Figures 7 a, b, and c.

Figure 52a: Floor 1 Thermal Zone Floor Plan

Figure 7b: Floor 2 Thermal Zone Floor Plan

Figure 7c: Floor 3 Thermal Zone Floor Plan

As can be seen from the divisions of the thermal zones, there are no core zones in this building. Thus, the infiltration rate and schedule for each zone is the same. With an infiltration rate of 0.33 ACU, the infiltration schedule is 1 when the HVAC system is off and 0.25 when the HVAC system is off.

However, not every zone is a conditioned zone connected to the HVAC system. On floor 1, the mechanical room does not need to be conditioned to the same standards as other areas constantly occupied by people. On each floor, the toilets also do not need conditioning, only ventilation, so the toilets are also not included in the HVAC system or contribute to the load. Thus, the conditioned building area is 2985.00 m2, and the entire building area is 3421.75 m2. Table 1 shows a summary table of the thermal zones, including area, usage type, and whether or not the zone is conditioned. Table 44: Thermal Zone Summary

Zone F1 Toilet F1 Media Center F1 Lobby F1 Classroom F1 Mechanical Room F1 Cafeteria F1 Kitchen F1 Office F2 Toilet F2 Office F2 Laboratory F2 Classroom F3 Toilet F3 Office F3 Classroom F3 Laboratory

Usage Type Restroom Media Center Reception Classroom Mechanical Room Cafeteria Kitchen Closed Office Restroom Open Office Laboratory Classroom Restroom Open Office Classroom Laboratory

Area (m2) 131 249 243 88 43 167 94 125 131 325 357 327 131 323 143 213

Conditioned (Y/N) N Y Y Y N Y Y Y N Y Y Y N Y Y Y

3.1.2. Occupancy Different room usages lead to a different occupancy density per room type. The default Design Builder occupancy densities are in compliance with ASHRAE 90.1-2010. Table 2 summarizes the different densities based on room usage. Unconditioned zones, the toilets and mechanical rooms, did not have any occupancy. Table 45: Occupancy Density by Room Usage

Usage Type Cafeteria Classroom/Media Center Office Kitchen Reception Laboratory

Occupancy Density (m2/person) 0.93 3.72 18.58 18.59 9.29 9.41

Despite the differences in occupancy densities by room type usage, the schedule each zone followed remains the same. The schedule is suggested by ASHRAE 90.1-2010, found in Appendix G. It is 0 in the early morning, with a sharp increase when the work day begins, around 9 AM. There is a dip around 1 PM, coinciding with lunch hour, then comes down gradually when the work day ends, around 5 PM. On Saturday, the % occupancy is much lower than on a workday, but follows a similar trend. On Sundays and holidays, there is very little occupancy in the building. The schedule can be seen in Figure 8.

Figure 53: Occupancy Schedule

3.1.3. Internal Load Density & Schedule 3.1.3.1. Interior Lighting Lighting Power Density (LPD) is measured by W/m2 and the ASHRAE 90.1-2010 standard states different minimum LPD requirements for each room usage type. Table 3 shows the different LPDs used by room usage type. Table 46: Room Usage Type LPD (ASHRAE 90.1-2010 Table 9.6.1)

Room Usage Type

LPD (W/m2)

Office

11.8

Classroom

14.0

Cafeteria/Food Preparation

12.9

Restroom

9.7

Once the lighting power density was determined, another factor that plays into the energy consumption of a building is the lighting schedule. The lighting schedule of a building is often set automatically, so that every weekday would be exactly the same, regardless of how many people are actually in the building. The schedule extends to Saturday and Sunday, which have their own respective schedules. Design Builder models lighting schedule based on the percentage of lights that are on during the span of one hour. Each hour has a fraction indicating the percentage of lights that are on.

ASHRAE 90.1 â&#x20AC;&#x201C; 2010 Appendix G suggests a lighting schedule for normal a workday, Saturday, and Sunday/holiday. It is depicted in graphical form in Figure 9.

Figure 54: Lighting Schedule Usage Percentage

3.1.3.2. Interior Equipment Equipment Power Density (EPD) represents the energy usage from plug loads, like computers, desk lights, or printers. It is measured as an average W/m2 across each zone. The DOE Reference gives suggestions for EPD levels based on space type. The different EPDâ&#x20AC;&#x2122;s can be seen in Table 4. Table 47: Equipment Power Density by Space Type

Space Type Atrium Corridor/Transition Electrical/Mechanical Lobby: For Elevator Conference/Meeting/Multipurpose/Lab Restroom Food Service Classroom Closed Office

EPD (W/m2) 7.5 7.5 7.5 6.88 13.2 10.5 9 11 7.5

Once the equipment power density was determined for each zone, another factor that plays into the energy consumption of a building is the schedule on which the equipment is operated. The equipment usage corresponds directly to the occupancy, as each person has their own set of equipment plug loads. The schedule is operated in the same way as the lighting schedules, with percentages of use per hour, differentiated between weekdays, Saturday, and Sunday.

The equipment power schedule can be seen in Figure 10.

Figure 55: Equipment Power Schedule

3.1.4. Thermostat Set Point The building is not heated and cooled to the same temperature 24/7. Instead, there is a heating set-point and setback temperature. The set-point is the temperature the building is heated or cooled to when there are occupants in the building. The setback temperature is the temperature that the building is kept at when there are no occupants. For heating, the set-point is 21 degrees C with a set-back temperature of 15.6 degrees C. For cooling, the set-point is 24 degrees C with a set-back temperature of 26.7 degrees C. The heating and cooling schedules can be seen in Figure 11 a and b.

Figure 56: (a) Heating Schedule (b) Cooling Schedule

3.1.5. Flow Rate Setting The Domestic Hot Water (DHW) tap in the HVAC model controls the flow of hot water in the building under study. An important component of the DHW demand side loop is the water outlet, which defines the hot water consumption and delivery temperatures in the building. In detailed HVAC modeling, an important parameter for the water outlet is the peak flow rate. The peak flow rate (given as m3/s) defines the maximum flow rate of hot water in the building, which is multiplied with the defined flow rate fraction schedule. The product provides DesignBuilder with the

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On providing the above calculated value in DesignBuilder and running the simulation, however, we observed high water usage values in the model, indicating that the peak flow rate used from the source might not be realistic without the presence of realistic engineering sources. Upon further consultation and using results from DOE building tests representing a good majority of US office buildings, the peak flow rate per occupant was taken as 0.0025 gallons/minute, or 1.6 x 10-5 m3/s. Using this value, following recalculation was conducted: đ?&#x2018;&#x192;đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x2DC; đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x; đ?&#x2018;&#x153;đ?&#x2018;?đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;Ą đ?&#x2018;&#x201C;đ?&#x2018;&#x153;đ?&#x2018;&#x; đ?&#x2018;&#x201A;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;&#x2019; đ?&#x2018;Ąđ?&#x2018;Śđ?&#x2018;?đ?&#x2018;&#x2019; đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;&#x2013;đ?&#x2018;&#x2122;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D; = 0.0025 đ?&#x2018;&#x2020;đ?&#x2018;&#x153;, đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ?&#x2018;&#x192;đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x2DC; đ?&#x2018;&#x201C;đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;¤ đ?&#x2018;&#x;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2019; = 1.6đ?&#x2018;Ľ 10â&#x2C6;&#x2019;6

đ?&#x2018;&#x161;3 đ?&#x2018;

đ?&#x2018;&#x201D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;&#x203A; = 1.6đ?&#x2018;Ľ 10â&#x2C6;&#x2019;6 đ?&#x2018;&#x161;3 /đ?&#x2018; đ?&#x2018;&#x161;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;˘đ?&#x2018;Ąđ?&#x2018;&#x2019;

đ?&#x2018;Ľ 356 = đ?&#x;&#x17D;. đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x201C;đ?&#x;&#x201D;đ?&#x;&#x2014;đ?&#x;&#x201D; đ?&#x2019;&#x17D;đ?&#x;&#x2018; /đ?&#x2019;&#x201D;

This value was chosen as the final peak flow rate to given as input to the Domestic Hot Water loop in the designed HVAC models.

https://www.engineeringtoolbox.com/hot-water-consumption-person-d_91.html 91

3.2. Baseline Building Envelope The building envelope can be defined as the physical barrier separating the building indoors from the outside atmosphere, which includes the roof, walls, floors, slabs and windows. As the first step of our analysis, we developed a baseline construction assembly set. These different assembly parameters were chosen so they would strictly meet the ASHRAE 90.1.2010 standard. In the process of choosing the template, different construction assembly templates available in DesignBuilder were studied. Since the aim was to choose a template which could meet the cold climate requirements of Pittsburgh, the ‘CZ5 Nonresidential Baseline Constructions’ template was finalized for the baseline model. The CZ5 stands for Climate Zone 5, Pittsburgh’s climate category. Based on the MRQ building model, focus was placed on the external wall, roof, slab and glazing. The baseline envelope set is described below, in terms of U-value, insulation layers and thickness: Table 48 Baseline envelope construction parameters

Parameter

Value

Source

Exterior Wall Construction Layers

Steel-framed ASHRAE 0.75in Stucco +1.25m 0.625in (Appendix A) Gypsum Board+ 0.0865m Board Insulation

Thickness (m) R-Value (m2-K/W) U-Value (W/m2-K)

0.1418 2.741 0.365

Flat Roof Construction Layers

90.1.2010

Thickness (m) R-Value (m2-K/W) U-Value (W/m2-K)

Insulation entirely above the deck ASHRAE 0.1268m Board Insulation + 0.01m (Appendix A) Metal Deck 0.1369 3.659 0.273

Ground Floor Construction F-Value(W/m2-K)

Slab-On-Grade, Unheated 1.264

ASHRAE (Appendix A)

90.1.2010

Glazing Construction SHGC

Vertical, Non-Metal framing 0.400

ASHRAE (Appendix A)

90.1.2010

Light Transmission

0.560

U-Value (W/m2-K)

3.120

Note that the Window-to-wall ratio (WWR) was kept constant at 40% for all 3 cases in the analysis. This value was chosen based on previous research and results from analysis involving Revit and eQUEST. It is important that note that DesignBuilder.

3.3 Baseline HVAC System A detailed HVAC system for the MRQ building was designed on DesignBuilder, to study the energy consumption of the ventilation, heating and cooling loads in the model. HVAC system forms an essential component of the building’s energy requirement, and an efficient HVAC model can considerably reduce the overall energy usage and long-term operational costs. For our analysis, two alternatives - Constant Air Volume (CAV) and Variable Air Volume (VAV) type HVAC systems were developed and compared. The CAV with reheat type system was used for the baseline and proposed envelope model (discussed in later sections). The CAV model was developed for all three floors, with each floor having its own Air Loop, which provide cooling and heating to the different thermal zones on a floor. This was done to mitigate the heat losses in the air ducts and improve the overall efficiency of the HVAC. The zones exceeded from the HVAC system were the toilet (all 3 floors), the mechanical equipment room (floor1). The other major components of the are the Chilled Water Plant Loop (CHW) for providing cold water for cooling, Hot Water Plant Loop (HW) to provide hot water for heating and the Domestic Hot Water Loop (DHW) to provide hot water for hot water sinks, showers etc. These remain common for all three floors. The CHW loop uses an air-cooler chiller and the HW loop uses boiler for the designed CAV system. The detailed system requires us to input parameters for the different HVAC components such as supply pump, fuel type, efficiency, chiller type, and the available tutorial for HVAC system modelling were referred to, for obtaining these different input parameters5. The parameters modified for the CAV system can be summarized as follows: Table 49 CAV type HVAC system parameters

Air Loop Setpoint Manager Type AHU Night Cycle Control Type Cycling Run time (s) Economizer control type Economizer maximum limited dry bulb temperature (C) Fan total efficiency Pressure rise (Pa) Hot Water Loop Setpoint Manager Type Setpoint at outdoor low temperature (C) Outdoor low temperature (C) Outdoor high temperature (C) Rated Pump head (Pa)

2- Warmest 2-Cycle on any 1800 2- Fixed dry bulb 21.11 0.6 1200 10- Outdoor air reset 82.22 -6.67 10 162605

https://www.youtube.com/watch?v=7bz0pmnE67k 93

Pump Motor efficiency

0.70

Boiler template Nominal Thermal efficiency

ASHRAE 90.1 Appendix G baseline 0.80

Chilled Water Loop Setpoint Manager Type

10- Outdoor air reset

Rated Pump head (Pa)

162605

Pump Motor efficiency

0.70

Domestic Hot Water Loop Target temperature schedule

Always 43.3 C (Self-created)

Peak flow rate (m3/s) Flow rate fraction schedule Setpoint Variable Schedule Rated Pump head (Pa)

0.00005696 ASHRAE 90.1 Service Hot Water- Office Always 43.3 C (Self-created) 162605

Pump Motor efficiency

0.70

Water heater Setpoint temperature schedule Heater Fuel Type

Always 43.3 C (Self-created) 2-Natural Gas

Heater thermal efficiency

0.80

Design Loop exit temperature (C) Loop design temperature difference (deltaC)

60 5

Figure 57 DesignBuilder model for CAV type HVAC system

Figure 12 shows the developed DesignBuilder model for the baseline and proposed envelope HVAC system. We observe the three separate air loops for each floor, connected to the CHW and HW loop shown in blue and red respectively. The DHW loop is also modeled with a water outlet as shown in yellow. The nodal diagram for the CAV system is shown in Figure 13. The output SVG file from EnergyPlus can be used to represent the block diagram of the CAV system, and is shown in Figure 14. A self-constructed CAV system block diagram is shown in Figure 15.

Figure 58 Nodal diagram for CAV system

Figure 59 Block diagram of CAV type HVAC system

Figure 60 CAV type HVAC block diagram

4. Alternative Envelope Model Description 4.1 Proposed Envelope Design The second section of the analysis involves modifying the baseline envelope assembly in order to improve the overall energy-efficiency of the building, keeping the HVAC system the same as baseline. In our previous analysis and results with energy modeling tools such as Revit and eQUEST, a linear relationship between the U-value of the envelope components and the building Energy Use Intensity (EUI) was observed. The same approach was considered for developing a proposed envelope model, where the U-value of the external wall, roof, slab and glazing was varied (reduced) to achieve a more energy-efficient alternative. For this, a simple parametric analysis was conducted, where the insulation layer materials and their thickness was carefully selected and varied to fit the requirements of a cold climate region such as Pittsburgh. For external wall and flat roof, a reduction in U-value resulted in an improved EUI value, but slab did not give any positive results with reduction in its U-value. For glazing, a metal frame template was chosen, based on previously conducted research, and reduced SHGC and visible transmittance value. This observation is similar to what was observed in the previous analysis with Revit and eQUEST. The modified envelope properties are summarized as follows: Table 50 Modified envelope construction parameters

Parameter

Value

Remarks

Construction Layers

Steel-framed 0.203m Brick +1.25m 0.625in Gypsum Board+ 0.0865m Board Insulation

Added a highly insulating brick layer with high thickness to replace the less effective Stucco layer

Thickness (m) R-Value (m2-K/W) U-Value (W/m2-K)

0.1418 3.008 0.332

Exterior Wall

Flat Roof Construction Layers Thickness (m) R-Value (m2-K/W) U-Value (W/m2-K)

Insulation entirely above the deck 0.25m Board Insulation + 0.01m Metal Deck 0.1369 7.082 0.141

Changed thickness of board insulation value to further reduce U-value while preserving the insulation properties of the material

Ground Floor Construction F-Value(W/m2-K)

Slab-On-Grade, Unheated 1.264

Continued with the baseline slab construction

Glazing Construction SHGC

Vertical, Metal framing 0.330

Chose a vertical, metal framing glazing template and

Light Transmission

0.770

U-Value (W/m2-K)

1.500

reduced the SHGC and Uvalue

As mentioned previously, DesignBuilder also assigns specific values to other construction parameters such as below-grade walls, pitched roof, basement and external floor, but these values are left unchanged during modification as the model under study does not include these components.

4.2. HVAC System Design Alternative In the alternative design with modified HVAC system, a variable air volume with terminal reheat supplied to multiple zones with a boiler and water-cooled chiller was developed. For water-cooled chiller, a cooling tower was used to condensing process. Different with CAV system, VAV supplies variable air volume to each thermal zone continuously with thermostat control. Supply air temperature is kept constant, supply air temperature controller in side the air duct controls the chilled water flow rate in air handling unit (AHU) shown in Figure 16. In order to supply variable air flow into different zones, fans should be variable volume fans. As shown in Figure 16, fan speed is regulated to maintain static pressure set point P, so there will be adequate but little pressure fluctuation at VAV box inlets. VAV box here is the terminal handling unit and it has a reheat coil in the alternative design to reheat the air and bring down the relative humidity.

Figure 61: Multi-zone VAV system with duct static pressure control

In the VAV system, there are two close-loop controls: one responsible for maintaining a constant supply air temperature and the other for regulating the supply flow rate to cope with room sensible load changes. The latter function may be achieved using various methods, including regulating the degree of opening an inlet guide vane at the inlet of the supply fan or regulating the running speed of the supply fan. The two control loops operate simultaneously.

When there is an increase in room sensible load following a steady operation period, the room temperature will start to rise. This will cause the fan to increase the supply flow rate. Before the cooling coil can adjust its output, the supply air temperature will start to rise. The supply air temperature controller will, in response, increase the chilled water flow rate through the cooling coil to restore the supply air temperature. The result, therefore, will be an increase in supply air flow rate with the supply air temperature kept at the desired level. The reverse will happen if there is a room sensible load reduction, i.e. a reduction in supply air flow rate, followed by a drop of supply air temperature and then a reduction in chilled water flow rate. Comparing to CAV system, the chiller used water cooled chiller with a condenser loop added into the system connecting with cooling tower. The input parameter for VAV systems are shown in Table 8. Hot water loop does not change from baseline. Table 51 Input Parameters in VAV system

Chilled Water Loop Chiller COP

5.5

Leave chilled water T [℃] Enter chilled water T [℃] Leaving chilled water T limit [℃]

6.67 29.4 2

Set-point Manager

Pump in water loop

Setpoint at outdoor low Temperature [℃] Outdoor low temperature [℃]

12.22

Type

15.56

Rated pump head [Pa]

variable speed 162605

Setpoint at outdoor high temperature [℃] Outdoor high temperature [℃]

6.67

Pup motor Efficiency

0.7

26.67

Condenser Loop Cooling Tower Type Evaluation Loss Mode

Condenser Pump Double Speed Saturated Exit

Pump Type

Setpoint Manager Min Setpoint T [℃]

21.11

Rated Pump Head [Pa]

variable Speed 235241.37

Max Setpoint T [℃]

29.44

Motor Efficiency

0.7

Offset temperature difference [℃]

1.5

Air Distribution Loop Fan Total Efficiency Pressure Rise [Pa] Motor Efficiency

0.6

Air Setpoint Manager

Air Handling Unit

1200

Min Setpoint T [℃]

Control Type

0.9

Max Setpoint T [℃]

Thermostat Tolerance [deltaC] Cycling run time [s] Extract Fan

Cycle on any 1 1800 Yes

Figure 17 shows the VAV system design in DesignBuilder. The loops at the top is chilled water loop connecting cooling tower and chiller. And the top right corner represents the domestic hot water system. And the red blocks at

the upper half of the figure shows the hot water loop that provides hot water to heat air up when heating is needed. The bottom represents zones with three air handling unit (AHU) for each floor. Apart from the schematic diagram, a block diagram and a nodal diagram were also created and shown in Figure 18 and Figure 20. Figure 19 shows another block diagram of VAV air loop show to show the detailed equipment and connections between them.

Figure 62: Alternative HVAC system design Schematic

100

Figure 63 VAV block diagram from EnergyPlus

101

Figure 64 VAV block Diagram 2

Figure 65 Nodal Diagram of VAV system

In the nodal diagram, notice there is a damper controlling air flow rate to each thermal zone and a reheat coil to achieve accurate control of humidity and temperature. Another difference with CAV system shown in Figure 20 is that the supply and return air fans are variable speed fans.

102

5. Simulation Result Comparisons 5.1 Baseline Model vs Proposed Envelope Model The developed baseline and proposed envelop models are simulated using EnergyPlus through the DesignBuilder interface, to obtain the necessary output data. We observe a 7.20% reduction in the Site Energy Use Intensity (EUI) by improving the building envelope. It is important to note that the DesignBuilder results provide the EUI value for only the conditioned area, so the total EUI value is calculated by using the annual site and source energy consumption given in the results. The calculated EUI values and resulting reduction can be summarized as follows: Table 52 EUI comparison summary for baseline vs proposed envelope with CAV model

Case

Site (kWh/m2)

Baseline Model (CAV) Proposed Envelope Model (CAV) % Reduction

362.15 335.86 7.20

EUI Source EUI (kWh/m2) 895.02 843.27 5.78

From the results, we see that there is a clear reduction in the overall building energy usage. The energy consumption takes place mainly in the form of electricity and natural gas, as indicated by the DesignBuilder results. The reduction is better understood by studying the annual energy consumption data for both model cases, as shown in Figure 21.

Energy Consumption (kWh)

Annual Energy Consumption by End-Use 500000.0 400000.0 300000.0 200000.0 100000.0 0.0 Heating

Cooling

Baselne Model

Interior Interior Lighting Equipment

Fans

Pumps

Water Systems

Proposed Envelope Model

Figure 66 Annual Energy Consumption Comparison for Baseline and Proposed Envelope CAV Model

The highest reductions are observed in heating, fan use and cooling, which can be explained by improved thermal properties of the building envelope. An improved envelope causes less heating and cooling losses from the building, which reduces the need for additional energy from the HVAC system. Also, the interior lighting and equipment loads are not affected by the envelope thermal properties, hence their consumption does not observe and change as we modify the construction assembly. Figures 22 and 23 display the variation in energy consumption of the different end-use applications in the building, for the baseline and proposed envelope (with CAV) model respectively. Comparing the two charts, we see that though the trends remain similar, there is an overall decrease in the energy requirement of heating and cooling loads.

103

Energy Consumption (kWh)

Monthly Energy Consumption by End-Use (Baseline Model) 160000 140000 120000 100000 80000 60000 40000 20000 0

Interior Lights

Interior Equipment

Fans

Pumps

Heating

Cooling

Water Systems

Figure 67 Monthly energy Consumption trend for Baseline Model

Monthly Energy Consumption by End-Use (Proposed Envelope) Energy Consumption (kWh)

140000 120000 100000 80000 60000 40000 20000 0

Interior Lights

Interior Equipment

Fans

Pumps

Heating

Cooling

Water Systems

Figure 68 Monthly energy Consumption trend for Proposed Envelope CAV Model

To better understand the heating and cooling energy trend for the different months, the data for the two indicators was extracted from DesignBuilder and analyzed, as shown in Figures 24 and 25. There is a clear reduction in the heating and cooling energy consumption with an improved construction assembly. For a cold climate region such as Pittsburgh, there is a threshold heating demand throughout the year, which grows considerably during the winter period. This trend is observed in the below figures as heating (in light blue) takes a U-shape in the curve, indicating its increase during winters, and falling demand during winters. An opposite trend is observed in the cooling energy, which reaches its peak during June-September period.

104

Monthly Variation in Heating Energy Consumption

Energy Consumption (kWh)

90000 80000 70000 60000 50000 40000 30000 20000 10000 0

Baseline

Proposed Envelope

Figure 69 Comparison of cooling energy consumption

Energy Consumption (kWh)

Monthly Variation in Cooling Energy Consumption 35000 30000 25000 20000 15000 10000 5000 0

Baseline

Proposed Envelope

Figure 70 Comparison of heating energy consumption

105

Seasonal variation in Heating Energy Consumption Energy Consumption (kWh)

400000 350000 300000 250000 200000 150000 100000 50000 0 Summer Baseline

Winter Proposed Envelope with CAV

Figure 71 Comparison of heating energy consumption during different seasons

Seasonal variation in Cooling Energy Consumption Energy Consumption (kWh)

140000 120000 100000 80000 60000 40000 20000 0 Summer Baseline

Winter Proposed Envelope with CAV

Figure 72 Comparison of cooling energy consumption during different seasons

Figures 26 and 27 further highlight the difference in heating and cooling energy consumption during different seasons of the year, with the difference in heating energy consumption more prominent than in cooling energy. This can be explained by the overall low use of cooling energy throughout the year, compared to heating, which makes the difference less apparent.

106

Table 53 Time Setpoint not met comparison for baseline vs Proposed envelope with CAV model

Thermal Zone FLOOR1:Media Center FLOOR1:Lobby FLOOR1:Classroom FLOOR1:Cafeteria FLOOR1:Kitchen FLOOR1:Closed Office FLOOR2:Open Office FLOOR2:Laboratory FLOOR2:Classroom FLOOR3:Classroom FLOOR3:Open Plan office FLOOR3:Classroom FLOOR3:Laboratory FLOOR1:Toilet FLOOR1:Mechanical Room FLOOR2:Toilet FLOOR3:Toilet Total

During Heating [hr] 4 34 4.5 0 20.83 24.67 23.5 13.33 3.17 2.5 33 5.17 12.83 0 0 0 0 181.5

From Table 11, we note that the number of set-point hours not met during heating, which sums to a total of 181.5 hours. The above data is for unoccupied hours, as the model can meet the required set-point time during the occupied hours. Figure 28 also provides a brief overview of the resulting cooling and heating design loads in the CAV type HVAC system. Due to the improvement in the building envelope, there was a considerable improvement in both cooling and heating loads, with the heating load reduction by almost 25%. As discussed previously, an energyefficient construction assembly greatly reduces the requirement for additional heating in the building, and a high heating load reduction further stresses on the high heating requirement throughout the year, a trend not observed in cooling.

Peak Design Load Comparison

Load (kW)

200 150 100 50 0 Cooling User Design Load (W) Baselne Model

Heating User Design Load (W)

Proposed Envelope with CAV model

Figure 73 HVAC sizing comparison for cooling and heating

107

(a)

(b)

Figure 74 End-use energy consumption for (a) Baseline Model and (b) Proposed envelope with CAV (in kWh/m2)

BuildSim visualization was also used to study the different models, and their resulting consumption trends. From the comparison chart in Figure 29, we do not observe significant difference in the split up, but on further analyzing the data, we observe a 6.6% reduction in fan energy use and 13.7% reduction in heating energy use. Figure 30 shows a comparison of the resulting building EUI with some commonly used standards, including a national office EUI benchmark, along with IECC and ASHRAE benchmark values. An appreciable reduction in the EUI value on applying the proposed envelope model also brings the new model EUI value closer to the accepted standard, though EUI remains higher than the other standards in both cases.

(a)

(b) Figure 75 Comparison of EUI with reference standards for (a) Baseline Model and (b) Proposed Envelope with CAV model (in kWh/m 2)

108

5.2 [CAV & Air-cooled chiller] vs [VAV & Water-cooled chiller] HVAC system contributes the most part of building electricity consumption in the world. For Hong Kong, HVAC system accounts for 30% in building electricity consumption. Thus, it is crucial to choose energy efficient HVAC system to reduce energy use intensity and carbon dioxide emissions of the building. This project conducted a simple parametric design for HVAC system. For the comparison of CAV with air-cooled chiller system and VAV with water-cooled chiller system, the simulation is based on proposed envelope assembly. So the design heating load and cooling load are the same as shown in Figure 38. Table 12 tabulates the annual site and source EUI consumption. VAV with water-cooled chiller system reduced 30.18% site energy consumption and 41.31% source energy consumption. Table 54 EUI summary for Proposed CAV vs Proposed VAV

Case

Site EUI Source EUI 2 (kWh/m ) (kWh/m2)

Proposed CAV with Air-cooled chiller Proposed VAV with Water-cooled chiller % Reduction

335.86 234.50 30.18%

843.27 494.91 41.31%

Figure 76 Comparison between proposed CAV & VAV system from BuildSimHub

Figure 31 shows the comparison of total site and source energy consumption, but the amount of energy is lower than reported in EnegyPlus since there is minor change in code when uploading idf file onto buildsimhub. But the reduction percentage are almost same. We regard EnergyPlus simulation report as the accurate one for reporting to energy consumption since buildsimhub relies on EnergyPlus too.

109

Figure 78 Energy Comparison of proposed CAV with standards (buildsimhub)

Figure 977:Energy Energy Comparison Comparisonofofproposed proposedVAV VAVwith withstandards standards (buildsimhub)

As shown in Figures 33 and 34, the proposed VAV model is closer to the benchmarking standards. The breakdown annual end use consumption was shown in Figure 34. It indicates that the most significant reduction is fan power, which is about one seventh of the CAV building model energy consumption. Since VAV system provides variable speed fan to supply variable volume of air into each zone, the fan is not running in full capacity, thus the electricity savings on fan operation is significant shown in Figure 34. But there is a slightly increase of pump energy, because water-cooled chiller needs pump to circulate water in the water loop.

Figure 79 Annual Energy Consumption Comparison for Proposed CAV and Proposed VAV Model, right figure from buildsimhub

The radar chart in Figure 34 compared the proposed CAV and proposed VAV models. Baseline color denotes the proposed CAV model and design color denotes proposed VAV model. It can be seen that fan power and cooling consumption reduced a lot while heating and pump consumption increased slightly. For the cooling energy consumption, the VAV with water-cooled chiller reduced more than 50%. It is because CAV-Multizone system supplies constant air volume to each zone with different temperature. When zone A has higher cooling load while zone B has a lower cooling load, CAV will supply lower temperature to fulfil zone Aâ&#x20AC;&#x2122;s

110

cooling load and use reheat coil in zone B to reheat the air up. Thus, simultaneous cooling and heating happens all the time in MRQ building. And this is a waste of energy. However, VAV is more energy efficient since it supplies variable volume of air with constant outlet temperature. So variable cooling load could be achieved by variable air volume supply. Therefore, reheat process is not necessary for VAV system, which saved a lot of energy. Regarding the slight increase of the heating energy consumption of VAV building case comparing to CAV building case, reheat coil could save energy by controlling temperature in individual thermal zones by not varying too much in supply air temperature in central air duct. And CAV system purely heats the building in winter while simultaneous cools and heats the building in summer. Thus, the energy is not wasted in heating for the CAV building case. So the heating consumption for CAV and VAV are similar. But VAV heating consumption should be smaller than CAV system, this is not normal. Further investigation is necessary for this case. The monthly energy consumption results are shown in Figures 35, 36, 37 and 38.

Figure 80 Monthly end use trend for Proposed CAV Model

Figure 81 Monthly end use trend for Proposed VAV Model

In Figure 35, the pump energy also increased from May to September because operation of chiller requires water circulation in the water loop.

111

Energy Consumption (kWh)

Monthly Comparison in Cooling Energy Consumption 35000 30000 25000 20000 15000 10000 5000 0

Proposed CAV

Proposed VAV

Figure 82 Comparison of cooling energy consumption

In summer seasons, cooling energy consumption reduced more than 60% for each month shown in Figure 37.

Energy Consumption (kWh)

Monthly Comparison in Heating Energy Consumption 90000 80000 70000 60000 50000 40000 30000 20000 10000 0

Proposed CAV

Proposed VAV

Figure 83 Comparison of Heating energy consumption

However, as explained above, heating load increased from October to April, which is not normal. Further investigation is needed for the case.

112

Figure 84 Design Loads Comparison

In Figure 39, the design loads for both case are same since the envelope and other input parameters are kept same. The maximum Design cooling (11.84 m3/s) and heating (8.48 m3/s) air flow are the same too. As shown in Table 13 and Figure 39, during occupied time, both models achieved 0 not met hours in respect of set point temperature. However, in Table 3, it shows the detailed not met hour in non-occupied hours for heating. It indicates that CAV with air-cooled chiller model has two times higher amount of hours that indoor area does not satisfy setback temperature. And this may cause the CAV system has lower heating consumption comparing to VAV system. The heating set point not met hour of CAV and VAV system are both below 300 hours according to ASHRAE standard, which is acceptable. Table 55 Time Setpoint Not Met Hours during non-occupied hours Time Setpoint Not Met Hour

During Heating(non-occupied) [hr] CAV+air-cooled chiller

VAV+water-cooled chiller

FLOOR1:MEDIACENTER1ST

FLOOR1:LOBBY1ST

2.67 9.5

FLOOR1:CLASSROOM1STSE

4.5

3.17

FLOOR1:CAFETERIA1ST

FLOOR1:KITCHEN1ST

20.83

7.33

FLOOR1:CLOSEDOFFICE1ST

24.67

6.83

FLOOR2:OPENPLANOFFICE2ND

23.5

6.83

FLOOR2:LABORATORY2NDN

13.33

FLOOR2:CLASSROOM2NDS

3.17

3.83

FLOOR3:CLASSROOM3RDS

2.5

4.17

FLOOR3:OPENPLANOFFICE3RD

9.5

FLOOR3:CLASSROOM3RDN

5.17

4.67

FLOOR3:LABORATORY3RDN

12.83

4.83

FLOOR1:TOILET1ST

FLOOR1:MECHANICALROOM1ST

FLOOR2:TOILET2ND

FLOOR3:TOILET3RD

Total Unmet Hours

181.5

71.33

113

Figure 85 Time Setpoint not met hours during occupied time, same for both proposed CAV and VAV models

After the comparative analysis of CAV and VAV air distribution system, the advantages and disadvantages of the two systems are summarized in Table 14. Table 56 Pros and Cons of CAV and VAV systems

CAV

VAV

Advantages

Disadvantages

Use for

➢ high degree of air quality control: temperature, relative humidity, gases, particulates, pressure, and air velocity.

➢ It involves simultaneous cooling and heating in summer —— waste of energy. ➢ RH may become too high at partload conditions ➢ Constant airflow affects the efficiency in zones where a slower flow rate would be sufficient

➢ For smaller spaces with few windows and uniform loads.

➢ Energy Efficient ➢ minimize operating cost of heating and cooling systems ➢ excellent for controlling air velocity & air quality & T

➢ constant Temperature ➢ high first cost

➢ For multiple zones control and not requiring high accuracy in humidity control

5.3. Baseline Building vs Final Modified Building (Amber Jiang) Since the best model was the proposed envelope with a VAV HVAC system, this section will compare the original baseline model with the best model to see the improvements. To begin, Table 15 shows the site and source EUI comparison between the two models at the entire building level, taken from the results given by Design Builder. Table 57: Baseline vs Best Case EUI Comparison

Baseline Proposed VAV % Improvement

Site EUI (kWh/m2) 361.12 234.49 35.1%

Source EUI (kWh/m2) 892.45 494.91 44.5%

As can be seen, there is a 35.1% reduction in site EUI and a 44.5% reduction in source EUI. These reductions include the changes made in the envelope, as well as the change from a CAV HVAC system to a VAV HVAC system. Most the improvement comes from the change to a VAV HVAC system, which uses much less energy for cooling.

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Using BuildSim Hub, the baseline case was also compared with the VAV system case. Figure 41 shows a graphical representation of the amount of absolute energy savings from both site and source, with the design model as the proposed envelope case with a VAV system.

Figure 86: Site and Source Energy Savings

Figure 42a shows the radar chart for the end use energy consumption breakdown in kWh. As can be seen, there is a significant decrease in cooling and fans in the design model (VAV model). Figure 42b shows the same information displayed in a stacked bar chart. Figure 42b shows the magnitude of the energy usage of the building which Figure 42a does not show. As can be seen, the total energy usage for the VAV model is significantly less than the total energy usage for the baseline model.

Figure 87: (a) Radar Chart for End Use Energy Consumption Breakdown (kWh) (b) Stacked Bar Chart for End Use Energy Consumption Breakdown (kWh)

Figure 43 shows the percentage comparison of the end use energy breakdown for the baseline and the VAV model. The magnitude of each end use can be seen when the user hovers over the chart, as can be seen in the example of cooling in Figure 43. Figure 43 gives a good visual overview of how the percentages of energy usages change when the model changes. The medium shade of green, which covers more than a third of the baseline, represents the energy consumption from the fans. As can be seen, in the VAV model, the energy consumption from the fans is not even 1/8 of the whole building energy consumption.

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Figure 88: Baseline and VAV model end use energy consumption breakdown

Thus, making a change to the envelope and switching the HVAC system can generally lead to significant savings for a medium sized mixed office/classroom building in a cold climate like Pittsburgh.

5.4 Benchmarking Comparison ASHRAE 90.1 – 2010 gives a benchmark EUI for a medium sized office building to be 117.7 kWh/m2 for site EUI. The benchmark EUI for a primary school building is 158.4 kWh/m2 for site EUI. Since the MRQ building is a mixed office and educational building, we would expect the benchmark to be in between 117.7 and 158.4 kWh/m2. In a national study conducted by the US Department of Energy’s Energy Information Administration (EIA), an average site EUI was calculated for several different types of buildings. They called it the EnergyStar Portfolio Manager, and it can be used as a benchmark to compare our building’s EUI with how the rest of the nation’s buildings’ EUI. Because it is a benchmark and not a standard, it is less stringent than ASHRAE, and thus, the benchmarked EUI is higher than the EUI given by ASHRAE. Given that the baseline site EUI was 361.1 kWh/m2 and the VAV model with the modified envelope site EUI was 234.5 kWh/m2, Table 16 shows how far away the models were from the ASHRAE standard and the EIA EnergyStar Portfolio Manager benchmark. Table 58:Benchmark Comparison with Baseline and Best Case

Benchmark

Site EUI (kWh/m2)

ASHRAE 90.1-2010 Medium 117.7 sized office ASHRAE 90.1-2010 Primary 158.4 Education EIA EnergyStar Portfolio 212.3 Manager - Office

% Baseline 206.8%

Difference % Difference Best Model 99.2%

128.0%

48.0%

70.1%

10.5%

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Buildsim created Figure 44, which shows how the best case model stacks up against existing standards, like ASHRAE 90.1 and IECC standards.

Figure 89: Buildsim Energy Comparison

Even with the best model, it still has not reached the building standard. To achieve this EUI standard set by ASHRAE, management of the building could consider putting in renewable energy sources to offset the amount of energy the building uses from the grid. For example, if solar panels were installed on the roof and as shading devices, not only would the cooling load be decreased because of the shading, but the solar panels would also generate energy.

6. Proposed VAV vs Proposed VAV with PV system The proposed envelope model with VAV system was further improved by incorporating a rooftop and shading-type PV system. The improved model simulation was run on DesignBuilder and BuildSim Hub, and the results are shown as follows: Figure 90 EUI summary for Proposed VAV vs Proposed VAV with PV model

Case

Site EUI (kWh/m2)

Source EUI (kWh/m2)

Proposed Model with VAV Proposed VAV with PV system

235.16 231.94

496.31 487.13

% Reduction

1.36%

1.84%

Thus, a reduction of 1.34% was observed in the site EUI on adding the PV system to the Proposed model with VAV system. Since the end-use demand in the building was not affected by the additional of a standalone PV system, the annual and monthly energy consumption trends did not observe any change, and are discussed in the previous section. However, there was an expected reduction in the amount of utility electricity used, which was observed from the DesignBuilder results as shown in Table 17. Table 59 Electricity generation source summary

Fuel-Fired Power Generation

Electricity [kWh] 0

Percent Electricity [%] 0

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High Temperature Geothermal

Photovoltaic Power Wind Power

17712.063 0

4.39 0

Power Conversion Net Decrease in On-Site Storage

-708.48 0

-0.2 0

Total On-Site Electric Sources Electricity Coming From Utility

17003.58 386116.744

4.22 95.78

Surplus Electricity Going To Utility Net Electricity From Utility

0 386116.744

0 95.78

Total On-Site and Utility Electric Sources

403120.324

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From Table 17, we observe that PV system provided 4.39% of electricity to the building in the last proposed section. However, this was also accompanied by an additional consumption of 708.48 kWh for DC-AC power conversion of the generated PV power. Thus, a net reduction of 2.35% was achieved in the total electrical energy consumed from the grid compared to the â&#x20AC;&#x2DC;proposed envelope with VAVâ&#x20AC;&#x2122; model. From Buildsim, we compared the VAV model without PV with the VAV model with PV. As can be seen in Figure 46, the overall magnitude of energy consumed did not change much, only 2.35%. When looking into the data, we saw that the pumps and cooling decreased slightly while heating increased slightly. This trend is consistent with the change in end use energy usage when comparing CAV with VAV.

Figure 91: End Use Energy Breakdown VAV vs VAV with PV

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7. Conclusion This project investigated the effect of different HVAC systems and envelope assemblies on building energy performance through simulation using Design Builder and EnergyPlus. The objective of the study is to understand detailed HVAC system design and apply the design in energy simulation. The project also aims to investigate the interactive effects between HVAC system design, enclosure design and other building energy consumption elements. The baseline envelope was designed using the ASHRAE 90.1.2010 compliant construction assembly template available in the library, coupled with a CAV type HVAC system. The envelope was then modified to a more energy-efficient alternative (keeping the CAV system intact), which led to a site Energy Use Intensity (EUI) reduction of 7.20%. A considerable reduction in the building heating and cooling energy was observed, decreasing the HVAC heating load by 25%. In terms of HVAC system design, two system were investigated. One is CAV with air-cooled chiller, another is VAV with water-cooled chiller. Comparative analysis was conducted and it is found that VAV with water-cooled chiller system achieved 30% reduction of site EUI and 40% reduction of source EUI. Further, a final comparison between the baseline and proposed envelope with VAV model showed a significant site EUI reduction of 35.1%, due to the combined energy-efficiency of a improved building envelope and VAV type HVAC system. This highlighted the positive impact of a well insulated construction assembly and VAV system on the energy use in a medium-size office building, in a cold climate region like Pittsburgh. To study the effects of on-site distributed generation on the building source energy consumption, a combined rooftop and shading type PV system was designed with the VAV type HVAC system. The results showed a 1.84% decrease in the building source EUI. This can be attributed to the on-site electricity generation due to PV, which accounted for 4.39% of the total electricity generation, and reduced the net source energy consumption (electricity from the utility grid) To summarize, for mixed use of office and educational building cases with similar climate, the increase of building envelope insulation and VAV air distribution with water-cooled chilled water system is recommended in order to save a significant amount of energy.

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