用强化学习提升建筑能耗管理，兼顾成本降低与运行安全。
Data-Driven Policy Mapping for Safe RL-based Energy Management Systems
- 通过聚类提取共性用电模式，实现策略跨建筑迁移无需重训。
- 结合LSTM预测未来状态，使智能体响应动态变化，成本降低最高15%。
- 引入领域约束动作掩码，保障探索过程安全，适合新建筑快速部署。
全球能源需求增长与可再生能源整合复杂度提升，使建筑成为可持续能源管理的核心。本文提出一种三步走的强化学习（RL）建筑能源管理系统（BEMS），融合聚类、预测与约束策略学习，解决可扩展性、适应性与安全性挑战。首先，对不可转移负荷曲线进行聚类，识别常见用电模式，实现策略泛化与迁移，无需为每栋新建筑重新训练。其次，集成基于LSTM的预测模块，提前预判未来状态，提升智能体对动态环境的响应能力。最后，采用领域知识驱动的动作掩码机制，确保安全探索与运行，避免有害决策。在真实数据上评估表明，该方法对特定建筑类型可降低运营成本最高达15%，保持稳定的环境性能，并能在有限数据下快速分类与优化新建筑。系统还能在不重新训练的前提下适应随机电价变化。整体框架实现了可扩展、鲁棒且低成本的建筑能源管理。
原文摘要 · Abstract (English)
Increasing global energy demand and renewable integration complexity have placed buildings at the center of sustainable energy management. We present a three-step reinforcement learning(RL)-based Building Energy Management System (BEMS) that combines clustering, forecasting, and constrained policy learning to address scalability, adaptability, and safety challenges. First, we cluster non-shiftable load profiles to identify common consumption patterns, enabling policy generalization and transfer without retraining for each new building. Next, we integrate an LSTM based forecasting module to anticipate future states, improving the RL agents' responsiveness to dynamic conditions. Lastly, domain-informed action masking ensures safe exploration and operation, preventing harmful decisions. Evaluated on real-world data, our approach reduces operating costs by up to 15% for certain building types, maintains stable environmental performance, and quickly classifies and optimizes new buildings with limited data. It also adapts to stochastic tariff changes without retraining. Overall, this framework delivers scalable, robust, and cost-effective building energy management.
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