摘要
日前光伏出力预测在实际应用中精度有待提升,且存在新建光伏电站数据缺乏难以预测的问题。为此,充分利用大语言模型(large language model,LLM)的推理优势和序列各维相关性信息,提出一种气象协变量注意力增强Time-LLM模型用于日前光伏出力预测。首先,通过填充和拼接构建融合气象协变量信息和光伏出力历史信息的模型输入序列。然后,通过所提协变量注意力模块融合气象协变量序列与光伏出力序列之间的相关性信息。最后,通过Time-LLM架构实现时间序列与文本序列的模态对齐,有效利用LLM的文本分析能力进行光伏出力时间序列的准确预测。在光伏出力公开数据集上进行算例分析,结果表明:所提模型不仅在测试集上预测准确率最高,还具有最低的零样本预测误差。所提方法既提高了日前光伏出力预测任务的准确率,也为解决新建光伏电站因历史数据匮乏而难以应用传统深度模型预测的问题提供了新思路。
To improve the accuracy of day-ahead photovoltaic(PV)power forecasting and mitigate the challenge of data scarcity in newly built photovoltaic power stations,leveraging both the reasoning capabilities of large language models(LLMs)and the correlation information across various dimensions of sequences,this paper proposes a meteorological covariate attention-enhanced Time-LLM model for day-ahead PV power prediction.Firstly,the input sequence is constructed by padding and concatenating historical PV power series with meteorological covariate series.Then,a novel covariate attention module is introduced to capture the cross-dimensional dependencies between meteorological variables and PV power sequences.Finally,the Time-LLM architecture is employed to achieve modality alignment between time-series and text sequences,effectively leveraging the textual analysis capabilities of LLMs for accurate PV power forecasting.Experimental results on public PV datasets demonstrate that the proposed model achieves superior forecasting performance and exhibits remarkable zero-shot learning capability.The proposed method not only improves the accuracy of day-ahead photovoltaic power forecasting,but also provides a promising solution for newly built PV plants with limited historical data,where traditional deep learning models often fail due to data scarcity.
作者
葛彦硕
周艳真
郭庆来
肖大军
徐遐龄
李鑫
刘涛
GE Yanshuo;ZHOU Yanzhen;GUO Qinglai;XIAO Dajun;XU Xialing;LI Xin;LIU Tao(Department of Electrical Engineering,Tsinghua University,Beijing 100084,China;Central China Branch of State Grid Corporation of China,Wuhan 430000,China)
出处
《中国电力》
北大核心
2025年第12期211-222,共12页
Electric Power
基金
国家电网有限公司科技项目(面向电网调度智能辅助的大模型关键技术研究,5108-202455044A-1-1-ZN)。
