Evolutionary Tuning of Building Model ParametersAaron GarrettJacksonville State University

Conclusion

Evolutionary approach reduces electrical…• monthly SAE by almost 20% (250 kWh)• hourly SAE by over 10% (700 kWh)• hourly RMSE by over 7%

Evolution is a search algorithm

• Type of beam search• Less vulnerable to local optima• Optimizes based on environment

Evolutionary computation

• Simulates evolution by natural selection• Genetic algorithms• Evolution strategies• Genetic programs• Particle swarm optimization• Ant colony optimization

• Problem domain information is invaluable

An evolutionary approach

• Individual: Building parameters• Fitness: Error between E+ output and

sensor data

What is an individual?• Defined by 108 real-valued parameters• Material

• Thickness• Conductivity• Density• Specific Heat• Thermal Absorptance• Solar Absorptance• Visible Absorptance

• WindowMaterial:SimpleGlazingSystem• U-Factor• Solar Heat

• ZoneInfiltration:FlowCoefficient• Shadow Calculation Frequency

What is the fitness?

Individual Model

Actual Building Data

ErrorFitness

How do they evolve?

Mom DadBrotherSister

How are offspring produced?

Thickness Conductivity Density Specific Heat

Mom 0.022 0.031 29.2 1647.3

Dad 0.027 0.025 34.3 1402.5

Brother 0.0229 0.029 34.13 1494.7

Sister 0.0262 0.024 26.72 1502.9

• Average each component

• Add Gaussian noise

EC parameters• Population size 16• Tournament selection (tournament size 4)• Generational replacement with weak elitism (1 elite)• Gaussian mutation (mutation rate 10% of variable range)• Heuristic crossover

Building model search space

• 108 dimensions• Effectively infinite because continuous-valued

• Limit here is 1024 simulations per search• Approximately what could be done in a weekend

on single-core processor• 1024 is incredibly small number of samples

How do we get more for less?

• EnergyPlus is slow• Full-year schedule• 8 – 10 minutes per simulation

• Use abbreviated 4-day schedule instead• Jan 1, Apr 1, Aug 1, Nov 1• 15 – 30 seconds per simulation

Will that even work?• 4 independent random trials• 1024 simulations per trial• Samples taken from high to low error

Monthly Electrical Usage

r = 0.94

Hourly Electrical Usage

r = 0.96

The less expensive approach

Individual Model

Actual Building Data

ErrorFitness

About that actual data…

• 2% of the 15-minute measurements failed• Monthly electrical usage• Just ignore missing data (treat as 0)

• Hourly electrical usage• Any hour containing a single failure was counted

as a failure (8%)• Failures were not counted in error measure

How good are the existing models?

Model Monthly SAE Hourly SAE Hourly RMSE

V7-A2 1276.340 6242.036 1.20594

28July2010 1623.364 8113.685 1.62455

V7-A2 28July20100

200

400

600

800

1000

1200

1400

1600

1800

1,276.3

1,623.4

Monthly SAE

V7-A2 28July20100

1000

2000

3000

4000

5000

6000

7000

8000

9000

6,242.0

8,113.7

Hourly SAE

V7-A2 28July20100.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.2

1.6

Hourly RMSE

Evolve using 4-day schedule• 8 independent trials• 1024 simulations per trial

V7-A2 28July20100

200

400

600

800

1000

1200

1400

1600

1800

1,276.3

1,623.4

1,078.8

1,415.2

Existing Evolved

Monthly SAE

15% 13%

60%

V7-A2 28July20100

1000

2000

3000

4000

5000

6000

7000

8000

9000

6,242.0

8,113.7

5,660.0

7,453.2

Existing Evolved

Hourly SAE

9% 8%

35%

V7-A2 28July20100.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.206

1.625

1.129

1.514

Existing Evolved

Hourly RMSE

6% 7%

26%

And the full year schedule?• Only run on hourly usage• 8 independent trials• 1024 simulations per trial

V7-A2 28July20100

1000

2000

3000

4000

5000

6000

7000

8000

9000

6,242.0

8,113.7

5,660.0

7,453.2

5,539.2

7,161.6

Existing Abbreviated Full

Hourly SAE

9% 8%11% 12%

V7-A2 28July20100.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.206

1.625

1.129

1.514

1.119

1.458

Existing Abbreviated Full

Hourly RMSE

6% 7%7% 10%

Combining the two…

EvolveEvolve

Serial evolution• 8 independent trials• 1024 simulations per trial• 768 simulations for abbreviated; 256 simulations for full

V7-A2 28July20100

1000

2000

3000

4000

5000

6000

7000

8000

9000

6,242.0

8,113.7

5,660.0

7,453.2

5,539.2

7,161.6

5,580.7

7,343.4

Existing Abbreviated Full Serial

11% 12%11% 9%

Hourly SAE

V7-A2 28July20100.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.206

1.625

1.129

1.514

1.119

1.458

1.123

1.497

Existing Abbreviated Full Serial

7% 10%7% 8%

Hourly RMSE

On-deck Circle

Combining a different way…

Parallel evolution• 8 independent trials• 256 simulations for full year schedule• 768 simulations for abbreviated schedule

V7-A2 28July20100

1000

2000

3000

4000

5000

6000

7000

8000

9000

6,242.0

8,113.7

5,580.7

7,343.4

5,596.6

7,270.4

Existing Serial Parallel

Hourly SAE

11% 9%10% 10%

V7-A2 28July20100.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.206

1.625

1.123

1.497

1.121

1.482

Existing Serial Parallel

Hourly RMSE

7% 8%7% 9%

A bit surprising…

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 4760

62

64

66

68

70

72

74

Trial 1Trial 2Trial 3Trial 4Trial 5Trial 6Trial 7Trial 8

Generation

Four

-day

SAE

25%

Conclusion

Evolutionary approach reduces electrical…• monthly SAE by almost 20% (250 kWh)• hourly SAE by over 10% (700 kWh)• hourly RMSE by over 7%

What’s next?• Incorporate machine learning as fast island• Include temperature errors in fitness• How should this be combined with electrical usage error?• Should the be optimized separately with EMO approach?