mlsanityData to 14 Oct 2026

EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference

arXiv 2512.21343 ↗First posted 14 Dec 2025eess.SY cs.AI cs.MAcited by 0

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Abstract (arXiv)

Advances in automated systems afford new opportunities for intelligent management of energy at household, local area, and utility scales. Home Energy Management Systems (HEMS) can play a role by optimizing the schedule and use of household energy devices and resources. One challenge is that the goals of a household can be complex and conflicting. For example, a household might wish to reduce energy costs and grid-associated greenhouse gas emissions, yet keep room temperatures comfortable. Another challenge is that an intelligent HEMS agent must make decisions under uncertainty. An agent must plan actions into the future, but weather and solar generation forecasts, for example, provide inherently uncertain estimates of future conditions. This paper introduces EcoNet, a Bayesian approach to household and neighborhood energy management that is based on active inference. The aim is to improve energy management and coordination, while accommodating uncertainties and taking into account potentially conditional and conflicting goals and preferences. Simulation results are presented and discussed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 54 references that are in the map. First posted on arXiv 14 Dec 2025.

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15 of 54 references.

References · 15

  1. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
  2. 2020Reinforcement Learning through Active Inferencecited by 47
  3. 2017Deep Active Inferencecited by 44
  4. 2020On the Relationship Between Active Inference and Control as Inferencecited by 42
  5. 2022pymdp: A Python library for active inference in discrete state spacescited by 40
  6. 2019Scaling active inferencecited by 30
  7. 2020Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildingscited by 26
  8. 2019A Review of Reinforcement Learning for Autonomous Building Energy Managementcited by 21
  9. 2024From pixels to planning: scale-free active inferencecited by 13
  10. 2023Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithmcited by 11
  11. 2024Probabilistic Inference in the Era of Tensor Networks and Differential Programmingcited by 4
  12. 2025Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A reviewcited by 3
3 more
  1. 2025Active Inference for Energy Control and Planning in Smart Buildings and Communitiescited by 2
  2. 2022Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Frameworkcited by 1
  3. 2024Modeling Sustainable Resource Management using Active Inferencecited by 1

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