电力建设2025，Vol.46Issue(11)：121-129,9.DOI:10.12204/j.issn.1000-7229.2025.11.011
基于时空特征提取与跨模态融合的光伏集群功率预测
Power Prediction of Photovoltaic Clusters Based on Spatio-Temporal Feature Extraction and Cross-Modal Fusion
摘要
Abstract
[Objective]Photovoltaic(PV)power forecasting is a critical component of grid-connected PV dispatch and optimization.However,existing forecasting methods inadequately capture the spatial correlations between power plants,particularly in scenarios with multiple plants exhibiting strong correlations,in which the prediction accuracy remains suboptimal.[Methods]To this end,this study proposes a PV power prediction model,the spatiotemporal multimodal fusion network(ST-MoFNet),that combines spatiotemporal feature extraction and a cross-modal fusion attention module.The model extracts spatiotemporal features from the spatial and temporal dimensions using a graph convolutional network(GCN)and a temporal convolutional network(TCN-Informer),respectively,and efficiently fuses multimodal information to capture the complex spatiotemporal dependencies among power plants using a cross-modal fusion attention module.[Results]The experimental results showed that the ST-MoFNet exhibited superior prediction performance compared with the other models,achieving the best results in 1-,3-,and 5-step predictions.The average R squared(R2)value was 0.896,with a substantial improvement in accuracy of 6-16%.[Conclusions]The combined ST-MoFNet model effectively solved the shortcomings of traditional prediction methods in cluster prediction through its advantages in spatiotemporal feature extraction and information fusion and significantly improved the accuracy and reliability of PV power prediction.关键词
光伏发电/图卷积/集群预测/InformerKey words
photovoltaic power generation/graph convolution/cluster prediction/Informer分类
动力与电气工程引用本文复制引用
王健,刘汇塬,张占喜,沈赋,王开正,蔡子龙..基于时空特征提取与跨模态融合的光伏集群功率预测[J].电力建设,2025,46(11):121-129,9.基金项目
This work is supported by the National Natural Science Foundation of China(No.52107097),Yunnan Fundamental Research Projects(No.202401AU070148),Yunnan Revitalizing Talent Plan(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(52107097) （No.52107097）
云南省基础研究专项-青年项目(202401AU070148) （202401AU070148）
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）
