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Urban energy design software development: Aiko Nakano (MIT), Bruno Bueno (Fraunhofer Institute for Solar Energy), Leslie Norford (MIT), Christoph Reinhart (MIT)

URBAN HEAT ISLAND EFFECT MODELING IN ENERGY SIMULATIONS

Seletar Farmway [T reference site]

Punggol Field

Urban Weather Generator [UWG] estimates the hourly urban canopy air temperature and humidity using weather data from simulation programs. The model is based on energy conservation principles and is a bottom-up building stock model.

Pasir Ris Dr

It has been tested for Basel, Switzerland; Toulouse, France[1]; and several neighborhoods in Singapore, shown here. Calculated urban air temperatures are compared with measurements from a network of weather stations in Singapore, representing a range of land uses, morphological parameters and building usages. The simulated results are accurate within the range of air temperature variability observed in different locations within the same urban area for all three tested cities.

Tampines St

Changi Airport

The performance of UWG is comparable to a more computationally expensive mesoscale atmospheric model. The model shows satisfactory performance for all weather conditions and for different reference sites, which validates UWG’s robustness and suitability as an urban simulation engine.

Limau Grove

Bideford Rd

model validation through singapore data [4]

Penang Rd urban boundary layer

urban boundary layer model: energy balance for the control volume calculates air T above urban canopy layer

vertical diffusion model: of air T above the weather station

rural sensible heat flux

Kim Cheng Rd

average diurnal cycle of canopy layer air temperatures calculated by UWG and observed at urban sites

radiation

urban sensible heat flux

2km

neighborhood boundary

Tsky

advection

The model overpredicts February daytime and underpredicts July nighttime air T.

advection canopy exchange

roof window

Trural

rural boundary layer

Tsky

Tsky

Tsky

simulated measured Seletar

wall

mass

road

road

urban canopy layer

reference weather station

28

urban canopy + building energy model: Town Energy Balance [2] scheme including its building energy model [3]

RMSE 0.9K MBE 0.5K

24 4:00

METEOROLOGICAL PARAMETERS daytime + nighttime boundary layer height

simulation parameters

July 2010 [SW monsoon period]

32

Tsky

rural station model: energy balance at the soil surface calculates

February 2010 [NE monsoon period]

obs Seletar

T [ C]

12:00

RMSE 1.2K MBE -0.5K

20:00

4:00

12:00

20:00

URBAN GEOMETRY PARAMETERS H

assumed uniform

=

uwg’s robustness for different reference stations

bldg

Asite

vertical to horizontal built ratio VH =

wtd

Asite

max

urban canyon URBAN MORPHOLOGY PARAMETERS

building perimeter P building footprint Abldg

internal heat gains [heat flux from AC is calculated by the model] building construction + road surface material properties

and is relative to the chosen reference site. Despite this, UWG is able to produce similar urban temperatures. This robustness is pertinent to the design tool as data may not be available for a site that captures climate conditions upwind of the city [i.e. Seletar Farmway data is not public]

weighted average building height [by footprint] hwtd

vegetation [trees + grass; trees are treated as shading devices for urban canyon] REFERENCE SITE PARAMETERS road surface material properties vegetation site area Asite

Seletar Farmway

Feb Jul

RMSE [K] 0.9 1.2

MBE [K] 0.5 (0.5)

Tmax [K] 1.8 2.5

Changi Airport

Feb Jul

0.9 0.8

0.6 (0.2)

1.2 1.0

POLICIES AND REGULATIONS ARTICULATED WITH MICROCLIMATIC DESIGN

model

[K] 0.2 0.2

measured [K]

0.2 0.2

0.3 0.3 0.3 0.3

software workflow 9.71

9.50

The software can estimate the effects of urban morphology, geometry, and surface materials on temperature and energy consumption. This enables urban designers to parametrically test built densities and vegetation for masterplanning. Urban

5.76 12.54

12.76

6.46

11.36

1.25 12.47

12.14

cool roof with energy and thermal implications of these interventions. An optimization tool is currently being developed to

14.90

1.48

1.20

2.59

2.65

16.73 1.25

1.19

2.83

2.87 2.67 1.31

1.11

As UWG requires over 50 parameters, sensitivity analyses are performed to identify the most important parameters and reduce the number of user inputs. An earlier study for Toulouse and Basel [mild climates] has shown that vertical to horizontal built ratio, horizontal building density, and vegetation are most sensitive. Additional study for Singapore [tropical] and Boston, MA [cold, shown below] are conducted to determine the most effective design strategies for each climate. key urban design and planning strategies to reduce energy use and thermal discomfort horizontal building density

vertical to horizontal built ratio

15

sensible anthropogenic heat

2.76 2.43

2.55 3.67 1.65

4.15 1.21 2.65

1.87

7.23

1.29

2.68 1.34

1.66

1.27

2.70

2.45

2.75 1.50

1.39 1.35

1.92 1.23

model a city with Rhino or obtain GIS model for existing cities

1.31 1.33

1.24

Extract UWG model parameters and

Run UWG to capture UHI and then energy simulation using umi

A number of simulation tools have been developed at MIT. The presented software will be available as a stand-alone tool and a plug-in for umi to expand its capabilities to parametrically

13 T [ C]

T [ C]

13

2.69

3.44

integral component of family of design tools

albedo + emissivity: green + cool roof

15

5.32

11

11 Thermal Diversion |

9 0:00

6:00

12:00

|

18:00

0:00

6:00

12:00

18:00

|

| 0:00

6:00

12:00

9 0:00

18:00

3.4

6:00

12:00

18:00

9.0

3.0 1

2

3

3.7 1

4.9 2

Low

Average

sensitivity analysis thermal and energy metrics

6.1 3

67.8 2

25.0 1

8.5

135.5 3

a

0.05 1 0.98

0.20 2 0.90

Green Roof

Concrete

Urban Modeling Interface

Archsim Energy Modeling

DIVA

urban-scale tool for energy, thermal comfort, etc for Rhino

Energy modeling plug-in for Grasshopper

Daylighting and energy simulation plug-in for Rhino

0.95 3 0.89 Cool Roof

#hr

1,000

References:

500

Journal of Building Performance Simulation 1-13. [2] V. Masson. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorology 94 (2000) 357-397.

--

0.1

0

.50

0

.90

1

.30

1

.70

2

.10

.50

2

2

.90

3

.30

Boston Financial District

UWG and E+ hourly results are compared against ‘base’ urban scenario which has model parameters extracted from GIS and

Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

[3] Bueno, B., et al. (2012). Development and evaluation of a building energy model

canopy level urban air temperature at the neighborhood scale. Accepted for Urban Climate.

can be downloaded from http://urbanmicroclimate.scripts.mit.edu

Urban Microclimate  

Urban energy design software development: Modeling energy efficient cities via neighborhood-scale energy balances. Presented at MIT Center...

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