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Evolutionary Tuning of Building Model Parameters

Evolutionary Tuning of Building Model Parameters. Aaron Garrett Jacksonville 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.

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Evolutionary Tuning of Building Model Parameters

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  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 Error Fitness Actual Building Data

  8. How do they evolve? Mom Sister Dad Brother

  9. How are offspring produced? • 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 r = 0.96 r = 0.94 Monthly Electrical Usage Hourly Electrical Usage

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

  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?

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

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

  19. Combining the two… Evolve Evolve

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

  21. Combining a different way… On-deck Circle

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

  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?

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