可再生能源2025，Vol.43Issue(12)：1609-1618,10.
基于多源数据和Transformer框架的超短期太阳辐照度预测模型
Ultra-short-term solar irradiance prediction model based on multi-source data and Transformer framework
摘要
Abstract
The intermittence and volatility of photovoltaic power generation threaten the stability of the power grid,and high-precision prediction can provide key support for power system scheduling.Solar irradiance prediction methods based on meteorological data and sky images have been widely studied,but the mining of multi-scale temporal features and sky image features is still very limited,and the correlation between features is not fully utilized.Therefore,this paper proposes an ultra-short-term solar irradiance prediction model based on multi-source data and Transformer framework.First,the long-term and short-term memory neural network-Transformer is used to learn the long-term and short-term temporal variation characteristics of multivariate meteorological sequences.Secondly,the convolution visual Transformer is used to model the global information and local structure of the sky image,and the deep image features are extracted.Finally,a cross-modal feature fusion module based on attention mechanism is proposed,which uses image features to represent temporal features,realizes cross-modal feature fusion,and outputs prediction results by decoder.Experimental results show that the proposed method has better prediction performance than the state-of-the-art methods.Compared with the case of only using meteorological data,the NRMSE of the model is reduced by 7.4%on average.The model significantly improves the prediction accuracy by mining the features of multi-source data(meteorological time series and sky images)and using the cross-modal attention mechanism to effectively mine their correlation.关键词
太阳辐照度预测/多源数据/天空图像/长短期记忆神经网络/Transformer/CvTKey words
solar irradiance prediction/multi-source data/sky image/long short-term memory neural network/Transformer/CvT分类
能源科技引用本文复制引用
蒋素琼,许培德,王怀远..基于多源数据和Transformer框架的超短期太阳辐照度预测模型[J].可再生能源,2025,43(12):1609-1618,10.基金项目
福建省自然科学基金(2022J01113). （2022J01113）
