Deep Reinforcement Learning-Based Optimal Building Energy Management Strategies with Photovoltaic Systems

Because of the spread of solar photovoltaic (PV) systems, a significant amount of research has been conducted on the development of efficient energy management methods. Significantly, the energy operation strategies are essential for residential buildings due to the difference between peak demand and solar power generation time. Therefore, we proposed a novel deep reinforcement learning-based model considering both, direct use of the generated energy to the buildings and selling to utilities to minimize the building's total energy operating cost in a residential building with PV-energy storage system (ESS) installed. To verify the performance of the proposed model, case studies such as rule-based, selling-only case, and consumption-only case were conducted, showing that the proposed model minimized energy operating costs.