中国电机工程学报2025，Vol.45Issue(18)：7193-7205,中插14,14.DOI:10.13334/j.0258-8013.pcsee.240941
基于地基云图与气象因素多模态融合的光伏功率预测方法
Research on Photovoltaic Power Prediction Based on Multimodal Fusion of Ground Cloud Map and Meteorological Factors
摘要
Abstract
The existing fusion methods of foundation cloud maps and meteorological factors are difficult to fully explore the correlation and complementarity between the two modes,resulting in the inability to effectively improve the accuracy of photovoltaic power prediction under severe irradiance changes in weather.In response to the above issues,this article proposes a photovoltaic power prediction method based on multimodal fusion of ground cloud maps and meteorological factors.Adopting an improved Transformer network by replacing the position encoding of the meteorological data input with a time series convolutional network(TCN)to enhance the network's feature extraction capability for meteorological data;By replacing the multi head attention mechanism module in the network decoder with a long short-term memory networks(LSTM)network module,the network's ability to predict temporal sequences is improved.By introducing guided attention mechanism and low rank multimodal fusion algorithm separately,further feature extraction and fusion of cloud map features and meteorological features can fully utilize the correlation and complementarity between different source data.The simulation results show that the average root mean square error(RMSE),mean absolute error(MAE),and R 2 values of the above method are 0.294,0.248,and 0.866 respectively,which improving the accuracy of photovoltaic power prediction under severe irradiance changes in weather.关键词
地基云图/多模态融合/光伏功率预测/时序卷积网络-Transformer-长短时记忆网络/气象因素Key words
foundation cloud map/multimodal fusion/photovoltaic power prediction/time series convolutional network(TCN)-Transformer-LSTM network/meteorological factors分类
信息技术与安全科学引用本文复制引用
邓芳明,刘涛,王锦波,高波,韦宝泉,李泽文..基于地基云图与气象因素多模态融合的光伏功率预测方法[J].中国电机工程学报,2025,45(18):7193-7205,中插14,14.基金项目
国家自然科学基金项目(52377103) （52377103）
江西省自然科学基金项目(20232BAB204064,20242BAB25284).Project Supported by National Natural Science Foundation of China(52377103) （20232BAB204064,20242BAB25284）
Natural Science Foundation of Jiangxi Province(20232BAB204064,20242BAB25284) （20232BAB204064,20242BAB25284）
