Pierre-Olivier Pineau

arXiv2026

What Drives Energy Use? Prices, Efficiency Policies, and the Demand Frontier

What drives cross-state differences in U.S. energy consumption? We combine LMDI decomposition, stochastic frontier analysis, and variable-importance methods on a panel of 50 states plus DC over the 2006--2022 period. The observed 12.8% decline in per capita energy use is driven almost entirely by intensity improvements. A variance decomposition attributes 63% of cross-state variation in log energy use to the demand frontier, 34\% to inefficiency above it, and 3% to noise. Within the frontier, energy prices account for roughly 26% of cross-state variation and state efficiency policies for about 13%, while GDP and climate together explain only around 10\%. Efficiency policies also operate through a second channel by reducing inefficiency, adding a further 6 percentage points to their total contribution. The results suggest that pricing and regulation are the primary drivers of cross-state energy use differences.

econ.GN↗

arXiv2025

Large Language Models integration in Smart Grids

Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well - but how exactly? By integrating various data streams - including real-time grid data, market dynamics, and consumer behaviors - LLMs have the potential to make power system operations more adaptive, enhance proactive security measures, and deliver personalized energy services. This paper provides a comprehensive analysis of 30 real-world applications across eight key categories: Grid Operations and Management, Energy Markets and Trading, Personalized Energy Management and Customer Engagement, Grid Planning and Education, Grid Security and Compliance, Advanced Data Analysis and Knowledge Discovery, Emerging Applications and Societal Impact, and LLM-Enhanced Reinforcement Learning. Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions. Ultimately, this review illustrates how LLMs can significantly contribute to building more resilient, efficient, and sustainable energy infrastructures, underscoring the necessity of their responsible and equitable deployment.

cs.CY↗