用主动推理实现楼宇与社区的智能能源控制，兼顾隐私与不确定性。
Active Inference for Energy Control and Planning in Smart Buildings and Communities
- 分层主动推理架构，分别处理楼宇和社区级能源管理。
- 在极端电价下仍保持稳定，优于强化学习基线。
- 适合关注隐私保护与不确定环境的工程控制系统设计者。
主动推理（AIF）作为应对不确定性的决策框架正崭露头角，但在工程应用中潜力尚未充分挖掘。本文提出一种新型双层AIF架构，同时解决楼宇级与社区级能源管理问题。基于自由能原理，各层级可自适应变化环境，在传感器信息有限且尊重数据隐私的前提下运行。我们将连续AIF模型与理想优化基准及强化学习方法对比，并在极端电价场景下测试社区级AIF框架。结果表明该模型对突发变化具有强鲁棒性。本研究首次展示分布式AIF在工程中的可行性，也为工程系统中隐私保护与不确定性感知控制策略开辟了新路径。
原文摘要 · Abstract (English)
Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty, yet its potential in engineering applications remains largely unexplored. In this work, we propose a novel dual-layer AIF architecture that addresses both building-level and community-level energy management. By leveraging the free energy principle, each layer adapts to evolving conditions and handles partial observability without extensive sensor information and respecting data privacy. We validate the continuous AIF model against both a perfect optimization baseline and a reinforcement learning-based approach. We also test the community AIF framework under extreme pricing scenarios. The results highlight the model's robustness in handling abrupt changes. This study is the first to show how a distributed AIF works in engineering. It also highlights new opportunities for privacy-preserving and uncertainty-aware control strategies in engineering applications.
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