EImprove - Optimizing Energy and Comfort in Buildings based on Formal Semantics and Reinforcement Learning

Heating, ventilation, and air-conditioning (HVAC) system's supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as 'if-then-that-else' rules that capture the domain expertise of HVAC operators, but they often have con...

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Veröffentlicht in:2021 58th ACM/IEEE Design Automation Conference (DAC) S. 157 - 162
Hauptverfasser: Verma, Sagar, Agrawal, Supriya, Venkatesh, R, Shrotri, Ulka, Nagarathinam, Srinarayana, Jayaprakash, Rajesh, Dutta, Aabriti
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 05.12.2021
Schlagworte:
Buildings
Energy resolution
Heating systems
HVAC
Reinforcement learning
Semantics
Training
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Zusammenfassung:Heating, ventilation, and air-conditioning (HVAC) system's supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as 'if-then-that-else' rules that capture the domain expertise of HVAC operators, but they often have conflicts that may lead to sub-optimal HVAC performance. We propose EImprove, a reinforcement-learning (RL) based framework that exploits these conflicts to learn a resolution policy. We evaluate EImprove through a co-simulation strategy involving EnergyPlus simulations of a real-world office setting and a formal requirement specifier. Our experiments show that EImprove learns 75% faster than a pure RL framework.
DOI:10.1109/DAC18074.2021.9586313