用联邦强化学习优化零碳建筑群储能，兼顾隐私与效率。
Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL
- 采用联邦强化学习协同控制多建筑储能系统。
- 联邦TRPO在无调参情况下媲美现有顶尖方法。
- 适合关注绿色能源与隐私保护的智能电网研究者。
本文研究基于建筑微电网的最优能源管理挑战，提出一种协作且隐私保护的框架。通过在定制版CityLearn环境与合成数据上模拟，评估PPO与TRPO两种强化学习算法在不同协作设置下的表现，以高效管理分布式能源资源（DERs）。目标是在降低能源成本和碳排放的同时保障隐私。实验表明，联邦TRPO在无需超参数调优的情况下，性能可媲美当前最先进的联邦强化学习方法。该框架验证了协作学习在实现能源系统最优控制策略中的可行性，推动可持续高效智能电网的发展。代码已开源。
原文摘要 · Abstract (English)
This work studies the challenge of optimal energy management in building-based microgrids through a collaborative and privacy-preserving framework. We evaluated two common RL algorithms (PPO and TRPO) in different collaborative setups to manage distributed energy resources (DERs) efficiently. Using a customized version of the CityLearn environment and synthetically generated data, we simulate and design net-zero energy scenarios for microgrids composed of multiple buildings. Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy. Experimental results demonstrate that Federated TRPO is comparable with state-of-the-art federated RL methodologies without hyperparameter tuning. The proposed framework highlights the feasibility of collaborative learning for achieving optimal control policies in energy systems, advancing the goals of sustainable and efficient smart grids. Our code is accessible \href{https://github.com/Optimization-and-Machine-Learning-Lab/energy_fed_trpo.git}{\textit{this repo}}.
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