Mehdi Mounsif, Benjamin Jauvion and Fabien Medard, Akkodis Research, France

The global focus on greenhouse gases reduction places a major role on electrification of systems. While replacing fossil fuels with clean electricity is extremely appealing, the non-negligible costs associated with extracting and transforming mineral resources into renewable energy production systems as well as their world-wide deployment must be considered. As such, this study presents a novel approach to integrating Large Language Models (LLMs) into energy demand simulation, addressing the complexities and variability of human behavior as well as its profound impact on energy systems. By leveraging LLMs to impersonate diverse characters with distinct psychological traits, we explore the plausibility of reactions, prompt sensitivity, and second-order dynamics through individual agent experiments. Furthermore, we introduce a framework for multiagent scenario investigation, where a shared limited volume of energy triggers a traumatic event if the average environmental sensitivity drops below a specified threshold. A thorough result analysis and discussion concludes this work and sheds light on the relevance and current limitations of integrating modern language models both in complex systems and decision-making processes as well as more specific energy demand estimation the formulation of sustainable energy strategies.

Large Language Models, Population Dynamics, Behavioral Simulation, Energy Transition.