Energy Management Strategy for Smart Homes Based on Deep Reinforcement Learning

This paper proposes an energy management algorithm based on the proximal policy optimization algorithm. The proposed algorithm simultaneously considers multi-energy time-shifting and cascade utilization to minimize energy costs and maintain indoor temperatures. In the proposed algorithm, an adaptive additional reward method is introduced to define the priority of different energy sources, which facilitates the exploration of the action space to achieve unconstrained energy cascade utilization. Meanwhile, the adaptive additional reward method exploits the additional reward selectively to avoid suboptimal results. The simulation results demonstrate that the proposed algorithm can significantly reduce energy costs and has superior performance in energy cascade utilization.