Inverse Reinforcement Learning models household electricity consumption shifts

Researchers have utilized Inverse Reinforcement Learning (IRL) to model household electricity consumption behavior in Italy, treating households as agents interacting with their environment. The study aimed to understand how socioeconomic and climatic factors, such as an energy crisis and heat waves, influence these agents' reward functions and, consequently, their consumption patterns. By analyzing data from the summers of 2021, 2022, and 2023, the research identified distinct consumer groups exhibiting transient adjustments, durable shifts, or negligible changes in their cooling behavior. The findings suggest that energy policies should consider not only consumer demographics and location but also their timing of consumption and the persistence of behavioral responses to shocks. AI

IMPACT This research demonstrates a novel application of IRL for understanding complex human behavior in response to environmental and socioeconomic factors, potentially informing more adaptive energy policies.

RANK_REASON The cluster contains a single academic paper detailing a novel application of Inverse Reinforcement Learning to model household electricity consumption. [lever_c_demoted from research: ic=1 ai=1.0]

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• energy crisis
• heat wave
• inverse reinforcement learning
• Italy
• Summer 2021
• Summer 2023

• paper
• other

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