# Active Inference for Energy Control and Planning in Smart Buildings and Communities

arXiv: https://arxiv.org/abs/2503.18161 · HTML: https://mlsanity.com/paper/2503.18161/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)

## Built on by (most cited first, 2 of 2)

- [EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference](https://mlsanity.com/paper/2512.21343/) (2025)
- [One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware](https://mlsanity.com/paper/2610.19790/) (2026)

## Abstract (arXiv)

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.