Deep Reinforcement Learning-Based Demand Response for Smart Facilities Energy Management

This work proposes a novel deep reinforcement learning (DRL)-based demand response algorithm for smart facilities energy management to minimize electricity costs while maintaining a satisfaction index. Specifically, to accommodate the characteristics of the decision-making problem, long short-term memory (LSTM) units are adopted to extract discriminative features from past electricity price sequences and fed into fully connected multi-layer perceptrons (MLPs) with the measured energy and time information, then a deep Q-network is developed to approximate the optimal policy. After that, an experimental setup is constructed to investigate the effectiveness of the proposed DRL-based demand response algorithm to bridge the gap between theoretical studies and practical implementations. Numerical results demonstrate that the proposed algorithm can handle energy management well for multiple smart facilities. Moreover, the proposed algorithm outperforms the model predictive control (MPC) strategy and uncontrolled solution and is close to the theoretical optimal control method.