A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters

Abstract

With the rise in household computing power and the increasing number of smart devices, more and more residents are able to participate in demand response (DR) management through the home energy management system (HEMS). However, HEMS has encountered challenges in developing the most effective energy management strategies, including the complexity of modeling user comfort, uncertainty in electricity price and photovoltaic (PV) output, and the challenge of solving high-dimensional time-coupled decision problems. To address these challenges, a novel data-driven deep reinforcement learning (DRL)-base HEMS optimization framework considering uncertain household parameters is proposed. Firstly, a thermal comfort evaluation model based on integrated learning is proposed. Then, a prediction model based on the bidirectional gated recurrent unit neural network (BiGRU-NN) algorithm is proposed to mine the time series PV output and electricity price data. Finally, combining the PV output and electricity price forecasting, along with the thermal comfort evaluation, an optimal decision-making method based on soft actor-critic (SAC) algorithm for the HEMS is established. The results of numerical experiments show that the proposed method can effectively solve the high-dimensional integrated decision-making problem with uncertainty. By participating in DR, the household electricity cost can be reduced by 17.7% and the total cost can be reduced by 8.4%. Furthermore, the comparison result shows that the method proposed in this paper performs better than the existing optimization models based on proximal policy optimization (PPO) algorithm and twin-delayed depth deterministic policy gradient (TD3) algorithm.