EImprove - Optimizing Energy and Comfort in Buildings based on Formal Semantics and Reinforcement Learning
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Embedded System Design Methodologies
Embedded Systems
DescriptionHeating, 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.