[2506.12664] Behavioral Generative Agents for Energy Operations

Computer Science > Artificial Intelligence arXiv:2506.12664 (cs) [Submitted on 14 Jun 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Behavioral Generative Agents for Energy Operations Authors:Cong Chen, Omer Karaduman, Xu Kuang View a PDF of the paper titled Behavioral Generative Agents for Energy Operations, by Cong Chen and 2 other authors View PDF HTML (experimental) Abstract:Problem definition: Accurately modeling consumer behavior in energy operations is challenging due to uncertainty, behavioral heterogeneity, and limited empirical data-particularly in low-frequency, high-impact events. While generative AI trained on large-scale human data offers new opportunities to study decision behavior, its role in operational applications remains unclear. We examine how generative agents can support customer behavior discovery in energy operations, complementing rather than replacing human-based experiments. Methodology/results: We introduce a novel approach leveraging generative agents-artificial agents powered by large language models-to simulate sequential customer decisions under dynamic electricity prices and outage risks. We find 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 in both operational decis...