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

2025-03-23

AI Summary

This study addresses the challenges of partial observability, low sensor coverage, and data privacy constraints in smart building and community energy management. Methodologically, it introduces, for the first time, a continuous active inference (AIF) framework grounded in the free-energy principle into engineered energy systems—integrating hierarchical Bayesian inference, uncertainty-aware modeling, and online adaptive policy optimization to yield a privacy-preserving, robust, and adaptive hierarchical decision architecture. Experimental results demonstrate that the framework exhibits strong robustness under extreme electricity price perturbations, significantly outperforming reinforcement learning baselines and approaching the performance of perfect-optimization benchmarks. Moreover, it achieves low sensing overhead while maintaining high disturbance rejection capability. Collectively, this work establishes a novel paradigm for real-time, privacy-sensitive energy control and planning in distributed infrastructure systems.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

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.

Problem

• Develops dual-layer AIF for energy management in buildings and communities
• Addresses partial observability and data privacy in energy control
• Validates robustness of AIF in extreme pricing and uncertainty scenarios

Innovation

• Dual-layer AIF architecture for energy management
• Free energy principle adapts to evolving conditions
• Privacy-preserving control without extensive sensor data