Active Inference for Energy Control and Planning in Smart Buildings and Communities
This work addresses energy control and planning for smart buildings and communities, offering a novel application of Active Inference in engineering with potential for privacy-preserving strategies.
The authors tackled energy management in smart buildings and communities by proposing a dual-layer Active Inference architecture, which demonstrated robustness in handling abrupt changes and outperformed a reinforcement learning approach in validation tests.
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.