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

arXiv: https://arxiv.org/abs/2512.21343 · HTML: https://mlsanity.com/paper/2512.21343/

## 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.

## Builds on (most cited first, 15 of 15)

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Reinforcement Learning through Active Inference](https://mlsanity.com/paper/2002.12636/) (2020)
- [Deep Active Inference](https://mlsanity.com/paper/1709.02341/) (2017)
- [On the Relationship Between Active Inference and Control as Inference](https://mlsanity.com/paper/2006.12964/) (2020)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings](https://mlsanity.com/paper/2006.14156/) (2020)
- [A Review of Reinforcement Learning for Autonomous Building Energy Management](https://mlsanity.com/paper/1903.05196/) (2019)
- [From pixels to planning: scale-free active inference](https://mlsanity.com/paper/2407.20292/) (2024)
- [Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm](https://mlsanity.com/paper/2305.05484/) (2023)
- [Probabilistic Inference in the Era of Tensor Networks and Differential Programming](https://mlsanity.com/paper/2405.14060/) (2024)
- [Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review](https://mlsanity.com/paper/2503.05022/) (2025)
- [Active Inference for Energy Control and Planning in Smart Buildings and Communities](https://mlsanity.com/paper/2503.18161/) (2025)
- [Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework](https://mlsanity.com/paper/2211.15858/) (2022)
- [Modeling Sustainable Resource Management using Active Inference](https://mlsanity.com/paper/2406.07593/) (2024)

## 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.