电力建设2026，Vol.47Issue(9)：1-14,14.DOI:10.12204/j.issn.1000-7229.2026.09.001
大语言模型赋能的气象Agent构建及其在短期负荷预测中的应用
LLM-Empowered Meteorological Agent Construction and Its Application in Short-Term Power Load Forecasting
摘要
Abstract
[Objective]As the core driving factors for short-term power load forecasting,multivariate meteorological elements still confront prominent constraints in the acquisition and practical application of refined meteorological data.Accordingly,this paper investigates meteorological Agents empowered by large language models(LLMs)and applies them to short-term load forecasting.[Methods]First,a workflow agent integrated with large language models is constructed via the LLM development platform,which realizes the dynamic acquisition and intelligent parsing of structured meteorological information.This approach effectively improves the real-time response performance of the forecasting model and reduces the development and application complexity of short-term load forecasting systems.On this basis,a multimodal input framework for power system short-term load forecasting is established by integrating meteorological factors,power load time-series data,and calendar features.Combined with the inherent characteristics of time series and the gradient boosting tree algorithm,an optimized short-term power load forecasting model is further developed.Finally,the feasibility and effectiveness of the proposed model and algorithm are verified through experimental validation and case analysis.[Results]The proposed model and algorithm streamline the meteorological information-acquisition process and improve the forecasting accuracy.[Conclusions]This research provides a novel technical idea for the low-cost acquisition and efficient utilization of meteorological data in power system operation and analysis.关键词
大语言模型(LLM)/短期负荷预测/气象Agent/工作流Key words
large language model(LLM)/short-term power load forecasting/meteorological agent/workflow分类
信息技术与安全科学引用本文复制引用
蔡子龙,林红娅,沈赋,毛肖,王健,杨宇林..大语言模型赋能的气象Agent构建及其在短期负荷预测中的应用[J].电力建设,2026,47(9):1-14,14.基金项目
This work is supported by National Natural Science Foundation of China(No.62563016),Yunnan Applied Basic Research Project(No.202601AS070059,No.202501AT070350),Yunnan Xingdian Talent Support Program(No.KKRD202204021)and High-level Platform Construction Project of Kunming University of Science and Technology(No.KKZ7202004004). 国家自然科学基金项目(62563016) （No.62563016）
云南省应用基础研究计划项目(202601AS070059,202501AT070350) （202601AS070059,202501AT070350）
云南省兴滇英才支持计划项目(KKRD202204021) （KKRD202204021）
昆明理工大学高层次人才平台建设项目(KKZ7202004004) （KKZ7202004004）
