Abstract
The growing complexity of thermal power generation systems demands advanced forecasting solutions capable of integrating data analysis, model selection, and interpretability. This study proposes a modular large language model (LLM) agent framework for time series forecasting, designed to operate locally and interactively through natural language instructions. The framework incorporates a domain‐specific time series agent that was developed to automate data preprocessing, anomaly detection, and forecasting tasks using neural and statistical models. Experiments demonstrated the agent's capacity to autonomously conduct end‐to‐end analyses, achieving accurate forecasts with minimal user intervention. The PatchTST model, automatically selected by the agent, yielded the lowest mean squared error among evaluated methods. Results highlight the potential of LLM‐based agents to enhance transparency, usability, and reproducibility in energy forecasting pipelines.