Work

Versatile and self-adapting smart home control

Year: 2025

Type: conference-paper

Abstract: The research focuses on the control of a residential photovoltaic and battery system, designed to mitigate uncertainties from the distribution grid perspective while following prescheduled power profiles at the residential meter level. Specific attention is paid to reduced communication requirements, contrary to the traditional energy management strategy. This autonomy is achieved here using reinforcement learning, which has proven effective in the dynamic management of energy systems. The controller’s effectiveness is compared to other methods, such as rule-based controls and optimization approaches. In the use case, the objective is to follow in real-time a power profile based on the moving average of the net load over a week. The controller’s decision-making process is driven by a proximal policy optimization agent, which uses sequential temporal inputs such as the target power profile, the state of charge, and the net load data from the previous time step. To enhance the performance of the controller, the research explores the integration of a neural network architecture inspired by image processing techniques. Performance evaluation criteria include the daily energy deviation and the daily avoidable energy deviation from the target profile.

Source: IET conference proceedings.

DOI: 10.1049/icp.2025.1483

Authors: Fabien Sanchez, Vincent Debusschere, Rémy Rigo-Mariani, Antoine Labonne

Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Laboratoire de Génie Électrique de Grenoble

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: green

Funder: Agence Nationale de la Recherche