Understanding electricity consumption behaviour through Inverse Reinforcement Learning

2026-07-03

AI Summary

This study addresses the challenge of accurately capturing households’ nonlinear electricity consumption behaviors under socioeconomic and climatic shocks by modeling them as agents and, for the first time, applying inverse reinforcement learning to infer their latent reward functions from smart meter data. Integrating behavioral clustering with temporal analysis, the research examines shifts in cooling-related electricity use among Italian households during the 2021–2023 energy crisis and heatwaves. The analysis reveals three distinct response patterns—transient adjustment, persistent change, and minimal variation—and demonstrates that heterogeneity in daily timing of electricity use remains a significant behavioral dimension even among households sharing similar socioeconomic and environmental contexts. These findings offer novel empirical grounding for designing more granular and effective energy policies.

Abstract

Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy policies in a changing climate and under geopolitical tensions. Consumers respond differently to thermal stress depending on income, consumption habits and the surrounding built environment, a nonlinear behaviour that most approaches oversimplify. In this study, households are treated as agents interacting with complex environments, and Inverse Reinforcement Learning is used to represent their consumption behaviour as model implied reward functions. Specifically, we observe how these reward functions change when households undergo socioeconomic and climatic shocks. The framework is tested on different clusters of electricity consumption profiles in Italy. Clusters' reward functions are retrieved and used to understand how cooling behaviour changes from summer 2021 to summer 2022 and 2023, before, during and after the energy crisis and a heatwave. We find that these shocks reshaped cooling behaviour heterogeneously across consumer groups, in directions conditioned by their prior habits and built environment. Across the 2021 to 2023 summers, we identify a spectrum of responses: transient adjustments that receded as the shocks eased, durable shifts persisting into 2023, and consumers exhibiting negligible change. At the intradaily scale, groups comparable in socioeconomic and environmental context but differing in their daily timing of consumption responded distinctly, identifying time of use as a separate dimension of behavioural heterogeneity. Energy policies and demand-response schemes should therefore account not only for who consumers are and where they live, but for when they consume and whether their response to a shock persists.

Problem

• electricity consumption behaviour
• socioeconomic shocks
• climatic drivers
• behavioural heterogeneity
• demand response

Innovation

• Inverse Reinforcement Learning
• electricity consumption behaviour
• behavioural heterogeneity
• reward function
• demand response