Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.
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Transcript of Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.
![Page 1: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/1.jpg)
Evolutionary Tuning of Building Model ParametersAaron GarrettJacksonville State University
![Page 2: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/2.jpg)
Conclusion
Evolutionary approach reduces electrical…• monthly SAE by almost 20% (250 kWh)• hourly SAE by over 10% (700 kWh)• hourly RMSE by over 7%
![Page 3: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/3.jpg)
Evolution is a search algorithm
• Type of beam search• Less vulnerable to local optima• Optimizes based on environment
![Page 4: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/4.jpg)
Evolutionary computation
• Simulates evolution by natural selection• Genetic algorithms• Evolution strategies• Genetic programs• Particle swarm optimization• Ant colony optimization
• Problem domain information is invaluable
![Page 5: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/5.jpg)
An evolutionary approach
• Individual: Building parameters• Fitness: Error between E+ output and
sensor data
![Page 6: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/6.jpg)
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
![Page 7: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/7.jpg)
What is the fitness?
Individual Model
Actual Building Data
ErrorFitness
![Page 8: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/8.jpg)
How do they evolve?
Mom DadBrotherSister
![Page 9: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/9.jpg)
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
![Page 10: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/10.jpg)
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
![Page 11: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/11.jpg)
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
![Page 12: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/12.jpg)
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
![Page 13: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/13.jpg)
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
![Page 14: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/14.jpg)
The less expensive approach
Individual Model
Actual Building Data
ErrorFitness
![Page 15: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/15.jpg)
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
![Page 16: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/16.jpg)
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
![Page 17: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/17.jpg)
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%
![Page 18: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/18.jpg)
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%
![Page 19: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/19.jpg)
Combining the two…
EvolveEvolve
![Page 20: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/20.jpg)
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
![Page 21: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/21.jpg)
On-deck Circle
Combining a different way…
![Page 22: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/22.jpg)
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%
![Page 23: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/23.jpg)
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%
![Page 24: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/24.jpg)
Conclusion
Evolutionary approach reduces electrical…• monthly SAE by almost 20% (250 kWh)• hourly SAE by over 10% (700 kWh)• hourly RMSE by over 7%
![Page 25: Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University.](https://reader037.fdocuments.in/reader037/viewer/2022102923/551b207e5503462e578b62c1/html5/thumbnails/25.jpg)
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?