Behavior of electricity consumption with Inverse Reinforcement Learning

Understanding how households adjust their electricity consumption in response to climatic and economic factors is a growing challenge for governments and energy sector companies. Traditional methods often simplify reality by assuming linear behaviors, but evidence shows that users react in complex and heterogeneous ways depending on their income, habits, and environment. This is where an advanced branch of artificial intelligence comes into play: inverse reinforcement learning (IRL). This technique allows inferring the implicit reward functions that guide consumer decisions, treating them as agents interacting with a changing environment. A recent study applied this approach to clusters of electricity consumption profiles in Italy, analyzing how underlying rewards changed during the energy crisis and a heatwave between 2021 and 2023. The results reveal highly diverse responses: some groups showed temporary adjustments that disappeared once the shocks eased, others evolved toward permanent changes, and some did not modify their behavior. Additionally, the time of day when electricity is consumed was identified as a key dimension of heterogeneity, even among similar socioeconomic groups. This wealth of information has direct implications for designing energy policies and demand response programs. Companies seeking to optimize their operations and offer personalized solutions can benefit from predictive models based on IRL. Implementing these systems requires a multidisciplinary approach that combines artificial intelligence for businesses with scalable platforms. Q2BSTUDIO, as a software development and technology company, offers custom applications that integrate AI agents capable of simulating consumption behaviors and optimizing energy management. Its AWS and Azure cloud services provide the necessary infrastructure to process large volumes of data and train complex models, while business intelligence services solutions with Power BI facilitate pattern visualization and decision-making. Furthermore, cybersecurity is essential when handling sensitive consumption data, so Q2BSTUDIO incorporates robust protocols into each custom software. Ultimately, the combination of advanced AI techniques and a complete technological ecosystem makes it possible to address the challenges of the energy transition with a data-driven approach focused on human behavior.