建筑能源管理团队终于有了可解释的AI方案——XRL框架在降低电费的同时让你看清决策逻辑，做楼宇自动化或智慧能源的开发者可以直接参考。
该论文提出了一种可解释的深度强化学习（XRL）框架，用于优化住宅建筑的能源管理，特别是配备光伏和储能系统的建筑。研究对比了在线策略（如A2C和PPO）与离线策略算法，发现前者在累积奖励和策略稳定性上更优。框架利用事后解释技术揭示黑箱模型的决策过程，不仅降低了电费，还提供了透明、可操作的见解。实验基于合成数据和德国KIT的Living Lab真实数据，验证了方法的有效性。
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems. Volatile power generation, varying electricity tariffs, and increased entities, e.g., PV systems, and heat pumps, have increased the complexity and made the system harder to operate. This leads to the demand for additional control and optimization routes including data-based controls, such as reinforcement learning. While deep reinforcement learning (DRL) has emerged as a promising solution to optimize building operations in dynamic and ever more complex environments, its black-box nature impedes user trust and practical adoption. This paper presents a framework for explainable deep reinforcement learning (XRL) applied to energy management in residential buildings. We demonstrate its usage on both synthetic data but also on real-world data from the Living Lab Energy Campus (LLEC) at KIT. We train and compare both on-policy and off-policy DRL agents on an expanded state space that incorporates real-time measurements (demand, PV generation, battery power, state of charge), external signals (dynamic electricity price, local weather data), calendrical and holiday indicators, and forecasts for demand and price. Our experimental results indicate that on-policy algorithms, particularly Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO), outperform off-policy methods in terms of cumulative rewards and policy stability. To explain these models, we employ post-hoc interpretation techniques to elaborate the learned control policies. Our findings demonstrate that the XRL framework not only reduces electricity costs through optimal battery management, but also provides transparent, actionable insights into the agent's decision-making process.