Energy simulation Clibration

Arch. 6241 Building Simulation Design Practice Instructor: Godfried Augenbroe

Energy Plus + EPC Calibration Study Smithgall Student Service Building Presented by: Tianyu Feng

INTRODUCTION Built Year: 1990 Floor area: 3957 m² Major Feature: i> 2 story-tall ii> mainly brick wall, metal roof finish iii> a 2 story tall atrium at NE corner closed with curtain wall iv> mostly office, one auditorium, and one atrium v>: district heating & cooling Ferst Art Center

Student Center

kWh/m2/yr

Simulation result

resource

real

E+ with DC/DH

EPC

E+ with own HVAC

Consumption

157.38

28.11

144.30

144.30

% off to real

0%

82.14%

8.77%

8.37%

kWh/m2/yr

Simulation result

resource

real

E+ with DC/DH

EPC

E+ with own HVAC

Consumption

89.29

89.01

55.20

59.60

% off to real

0%

0.31%

38.18%

8.37%

kWh/m2/yr

Simulation result

resource

real

E+ with DC/DH

EPC

E+ with own HVAC

Consumption

102.74

82.55

121.91

82.56

% off to real

0%

19.64%

18.67%

19.64%

Simulation result Start point •

Real data are quite different with simulation results

•

The difference of heating, cooling and electricity are not proportional, or no reasonable pattern at all

Energy Plus Calibration

I. Methodology

a. Select uncertain input and categorize uncertain level

b. Calibrate 3 times with different level of inputs

Calibration 1

Cooling setpoint T (2) Heating setpoint T (2) Infiltration; People activity People density; Light; Equipment

More uncertain

wall U roof U Win. U

Calibration 2

Calibration 3

II. Result

kWh/m2/yr

Energy Plus Calibration

kWh/m2/yr

Heating Real

E+

Cal1

Cal2

Cal3

157.4

28.1

82.2

125.9

134.0

20.0%

14.9%

0.0%

82.1% 47.8%

kWh/m2/yr

Cooling Real

E+

Cal1

Cal2

Cal3

89.3 0.0%

89.0 0.3%

114.4 28.1%

115.4 29.3%

123.7 38.5%

Electricity Real

E+

Cal1

Cal2

Cal3

102.7 82.5 71.6 0.0% 19.7% 30.3%

96.2 6.3%

79.7 22.4%

Energy Plus Calibration

kWh/m2/yr

II. Result

TOT.

Real

JAN.

FEB.

MAR

APR

MAY

JUN

JUL

E+ with DC/DH

57.9%

56.2%

48.9%

50.2%

37.2%

33.0%

30.6%

19.0% 32.2% 42.0% 53.4% 53.8%

42.9%

Cali. 1

25.6%

15.6%

8.6%

36.3%

31.0%

29.4%

26.2%

13.1% 28.7% 31.1% 18.9% 15.9%

23.3%

Cali. 2

0.1%

10.9%

20.6%

7.6%

8.3%

25.9%

21.1%

10.0% 15.9%

2.8%

8.9%

14.8%

3.4%

Cali. 3

3.2%

16.1%

26.8%

9.8%

12.1%

29.1%

24.6%

12.8% 20.4%

4.8%

12.7% 17.1%

3.4%

AUG

SEP

OCT

NOV

DEC

kWh/m2/yr

EPC Calibration and Comparison

kWh/m2/yr

Heating Real

E+

Cal1

Cal2

157.4 0.0%

143.6 8.8%

65.4 58.4%

129.6 17.7%

Cooling Real

E+

Cal1

Cal2

89.3 0.0%

55.2 38.2%

165.0 84.8%

221.9 148.5%

kWh/m2/yr

Electricity Real

E+

Cal1

Cal2

102.7 0.0%

121.9 18.7%

110.9 8.0%

107.3 4.4%

Total Real 349.4 0.0%

E+ 320.7 8.2%

Cal1 341.3 2.3%

Cal2 458.7 31.3%

Calibration Sensitivity in EPC Question What parameters are most sensitive to the result change in calibration ? a. Stp 1 Htg COP, Ctg COP, Infil., Appliance, Lighting, Occ, wall U, roof U, window U,

example

1. Only change 1 parameter at a time 2. Each must change to same energy usage

Each parameter get a range, which could allows them reach same energy level

1< Htg COP< â&#x2C6;&#x17E;

Reduce Del E by 3%

1< Htg COP< 3.33

1< Clg COP< â&#x2C6;&#x17E;

Reduce Del E by 3%

1< Clg COP< 2.66

b. Stp 2 Input all 9 parameters from step 1

In EPC, USE binary, Limit the amount of variable chosen to reach best possible calibration result e.g. when # of variable is 1, the most sensible para will be chosen

Generate a ranking

Calibration Sensitivity in EPC 1 variable

CLG COP

2 variable

CLG COP

Lighting

3 variable

CLG COP

Lighting

HTG COP

4 variable

CLG COP

Lighting

HTG COP

Appliance

5 variable

CLG COP

Lighting

HTG COP

Appliance

What if we set the range each parameter in a more realistic condition CLG COP Lighting HTGof COP Appliance roof U Infiltration with different capacities in influencing the results?

7 variable CLG COP Lighting HTG COP Appliance roof U Infiltration Window U 6 variable

8 variable 91variable variable

CLG COP CLG CLG COP COP

Lighting Lighting

2 variable

CLG COP

HTG COP

3 variable

CLG COP

HTG COP

Appliance

4 variable

CLG COP

HTG COP

Appliance Lighting

5 variable

CLG COP

HTG COP

Appliance Lighting

Infiltration

6 variable

CLG COP

HTG COP

Appliance Lighting

Infiltration

7 variable

CLG COP

HTG COP

Appliance Lighting

8 variable

CLG COP

HTG COP

Appliance Lighting

HTG COP HTG COP

Appliance Appliance

roof U roof U

Infiltration Infiltration

Wall U

Wall U

Swing Parameters Infiltration Wall U Infiltration

Window U Window U

Wall U

Window U Window U Roof U

Occupancy

Calibration Sensitivity in EPC Limitation • Other parameters in this model in for Smithgall, and it can not be used by most buildings.

• The calibrated parameter range may not be applied to other projects due the some additional conditions • The result is about QUALITY (ranking) not QUANTITY. • The weather/ climate zone is also an very important factor in this case.

Conclusion • This study has been limited to very specific condition, and most likely won’t conclude certain type of situation •

but at least, it still shows some level of sensitivity of these uncertain parameter

kWh/m2/yr

kWh/m2/yr

kWh/m2/yr

Problem faced

â&#x20AC;˘ Before start calibrating, check the input and original model IN DETAILS â&#x20AC;˘ If the original data has more than 10 times, fundamental problems might exist

Conclusion • The data from meter may not always be accurate • More calibrated input parameters doesn’t mean more accurate result • A reasonable range for each calibrated input parameter is important to the result • The resolution we want could determines the accuracy level of the same calibration result. = if (target is del. E =1, combine htg+ clg +ele, analyze each E) = if (target is utility bill =1, calibrate with unit price, consider Es)