EcoNet Introduces Active Inference for Multi‑Agent Household Energy Management

Global: EcoNet Introduces Active Inference for Multi‑Agent Household Energy Management

Researchers from several institutions have unveiled a Bayesian framework called EcoNet aimed at coordinating household and neighborhood energy resources. The work, authored by John C. Boik, Kobus Esterhuysen, Jacqueline B. Hynes, Axel Constant, Ines Hipolito, Mahault Albarracin, Alex B. Kiefer, and Karl Friston, was submitted to arXiv on 14 December 2025. The paper proposes a multi‑agent planning and control system that leverages active inference to address the complexity of modern home energy management.

Active Inference as a Decision Engine

EcoNet employs active inference, a Bayesian approach that integrates perception, action, and learning, to enable agents to predict future states of the energy system while accounting for uncertainty. By modeling weather forecasts, solar generation, and consumption patterns as probabilistic variables, the system can generate adaptive control policies that balance competing objectives.

Addressing Conflicting Household Goals

The authors identify two primary challenges: (1) households often have overlapping yet conflicting goals such as minimizing electricity costs, reducing greenhouse‑gas emissions, and maintaining comfortable indoor temperatures; and (2) decision‑making must contend with uncertain inputs like weather and generation forecasts. EcoNet’s architecture allows agents to encode conditional preferences and negotiate trade‑offs in real time.

Simulation Findings

Simulation experiments described in the abstract demonstrate that EcoNet can improve overall energy efficiency compared with baseline scheduling algorithms. Results indicate reductions in peak demand and cost while preserving temperature comfort within predefined bounds. The authors note that performance gains are most pronounced when uncertainty is explicitly modeled.

Potential Deployment Scenarios

Beyond individual homes, the framework is positioned for scaling to micro‑grids and utility‑level coordination. By enabling decentralized agents to share information, EcoNet could facilitate demand‑response programs and support integration of renewable resources across neighborhoods.

Limitations and Future Directions

The study acknowledges that the current evaluation relies on simulated data, and real‑world implementation would require robust communication infrastructure and privacy safeguards. Future work is expected to involve field trials, integration with existing Home Energy Management Systems, and exploration of additional objective functions such as battery degradation.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.