Behavioral Generative Agents for Energy Operations
This work addresses energy management simulations for improving policy design, but it appears incremental as it applies existing generative agent methods to a new domain.
The paper tackled the challenge of modeling consumer behavior in energy operations by introducing generative agents powered by large language models to simulate customer decision-making, finding that these agents behave more optimally in simpler scenarios but show variable performance as complexity increases.
Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This paper introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. We demonstrate that these agents behave more optimally and rationally in simpler market scenarios, while their performance becomes more variable and suboptimal as task complexity rises. Furthermore, the agents exhibit heterogeneous customer preferences, consistently maintaining distinct, persona-driven reasoning patterns. Our findings highlight the potential value of integrating generative agents into energy management simulations to improve the design and effectiveness of energy policies and incentive programs.