Predictive control optimization of household energy storage devices for load regulation and energy conservation

-In order to regulate the load peak of households and achieve energy conservation, this study proposes a household energy management system (HEMS). The proposed HEMS embeds the Selfattention mechanism in the LSTM network to predict the load demand accurately for the next time step. Based on the prediction information, the HEMS optimize the control of household energy storage devices by deep reinforcement learning (DRL) in real time. According to the experimental results during two testing periods, the HEMS reduces peak load by 19.85 % and 26.38 %, and reduces energy consuming by 26.28 % and 22.08 %, outperforming other predictive control frameworks. Additionally, it achieves 31.9 % reduction in electricity costs. It can be seen that the optimal control of energy storage devices by the proposed HEMS through the predictive control framework is effective for achieving household load regulation and energy conservation.