EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference
This addresses the problem of optimizing energy use in households for cost, emissions, and comfort, though it appears incremental as it applies an existing method to a specific domain.
The paper tackles the challenge of managing household energy resources under uncertainty and conflicting goals by introducing EcoNet, a Bayesian approach based on active inference, with simulation results showing improved energy management and coordination.
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.