Interpretable Hybrid Deep Reinforcement Learning-Based Energy Management in Low-Carbon Community Energy Systems With Temporal Attention Mechanism

This article proposes an interpretable deep reinforcement learning (DRL) method for energy management of low-carbon community energy systems (LCCES), which effectively addresses the transparency limitations caused by the black-box nature of traditional DRL neural network structures, thereby overcoming a key constraint in energy system applications. First, we develop a hybrid integer dynamic decision DRL algorithm to solve the low-carbon scheduling problem in community energy systems with continuous-discrete hybrid action spaces. Second, we construct an interpretable artificial intelligence framework, where the temporal attention mechanism is used to process and extract features to provide macro-level decision contribution analysis. These features are input into the decision tree for extracting device-level rules. Building upon this, we design an ensemble decision tree architecture with temporal attention mechanism to effectively identify critical time periods influenced by system inertia and energy fluctuations, thereby achieving interpretable optimization strategies while enhancing decision robustness under state fluctuations. Simulation results based on the independent test set demonstrate that, in comparison with alternative methods, the proposed approach yields a 22.1% cost reduction and a 32.4% carbon emission reduction rate relative to twin delayed deep deterministic policy gradient (TD3), and a 14.7% improvement in explanation accuracy compared with static decision trees.