1. Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University

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

3. Evolution is a search algorithm Type of beam search Less vulnerable to local optima Optimizes based on environment

4. Evolutionary computation Simulates evolution by natural selection Genetic algorithms Evolution strategies Genetic programs Particle swarm optimization Ant colony optimization Problem domain information is invaluable

5. An evolutionary approach Individual: Building parameters Fitness: Error between E+ output and sensor data

6. 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

7. What is the fitness? Individual Model Actual Building Data Error Fitness

8. How do they evolve? MomDadBrotherSister

9. How are offspring produced? ThicknessConductivityDensitySpecific Heat Mom 0.0220.03129.21647.3 Dad 0.0270.02534.31402.5 Brother 0.02290.02934.131494.7 Sister 0.02620.02426.721502.9 Average each component Add Gaussian noise

10. 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

11. 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

12. 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

13. 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

14. The less expensive approach Individual Model Actual Building Data Error Fitness

15. 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

16. How good are the existing models? ModelMonthly SAEHourly SAEHourly RMSE V7-A21276.3406242.0361.20594 28July20101623.3648113.6851.62455

17. Evolve using 4-day schedule 8 independent trials 1024 simulations per trial Monthly SAE 15%13% 60% Hourly SAE 9%8% 35% Hourly RMSE 6%7% 26%

18. And the full year schedule? Only run on hourly usage 8 independent trials 1024 simulations per trial Hourly SAE 9%8%11%12% Hourly RMSE 6%7% 10%

19. Combining the two… Evolve

20. Serial evolution 8 independent trials 1024 simulations per trial 768 simulations for abbreviated; 256 simulations for full 11%12%11%9% Hourly SAE 7%10%7%8% Hourly RMSE

21. On-deck Circle Combining a different way…

22. Parallel evolution 8 independent trials 256 simulations for full year schedule 768 simulations for abbreviated schedule Hourly SAE 11%9%10% Hourly RMSE 7%8%7%9%

23. A bit surprising… 25%

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

25. 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?