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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?