A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
Columbia Data Science Institute（哥伦比亚数据科学研究所）；Red River Clean Energy（红河清洁能源）；King Abdulaziz City for Science and Technology (KACST)（国王阿卜杜勒阿齐兹城市科学技术城）；National Science Foundation（国家科学基金会）
This paper proposes a few-shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real-time electricity prices. The approach aggregates system state information, including electricity demand, renewable generation, weather forecasts, and recent electricity prices, into a set of statistical features that are formatted as natural-language prompts and fed to an LLM along with general instructions. The model then determines the likelihood that the next day would be a spike day and reports a confidence score. Using historical data from the Texas electricity market, we demonstrate that this few-shot approach achieves performance comparable to supervised machine learning models, such as Support Vector Machines and XGBoost, and outperforms the latter two when limited historical data are available. These findings highlight the potential of LLMs as a data-efficient tool for classifying electricity price spikes in settings with scarce data.
Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning
Department of Electronics, Information and Bioengineering（电子信息与生物工程系）；Department of Management, Economics and Industrial Engineering（管理、经济与工业工程系）；CMCC Foundation- Euro-Mediterranean Center on Climate Change（CMCC基金会—地中海气候变化中心）；RFF-CMCC European Institute on Economics and the Environment（RFF-CMCC欧洲经济与环境研究所）；Centre for Research on Geography, Resources, Environment, Energy and Networks (GREEN)（地理、资源、环境、能源与网络研究中心）
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market
University College Cork（科克大学学院）；International Energy Research Centre（国际能源研究中心）；Tyndall National Institute（廷德尔国家研究所）；University of Edinburgh（爱丁堡大学）；SFI Insight Centre for Data Analytics（SFI数据洞察中心）
