Article(id=1309892220233286121, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750521600000, receivedDateStr=2025-06-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790232241924, onlineDateStr=2026-09-24, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790232241924, onlineIssueDateStr=2026-09-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790232241924, creator=13701087609, updateTime=1790232241924, updator=13701087609, issue=Issue{id=1309639316763149213, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='12', pageStart='1563', pageEnd='1704', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171945038, creator='13701087609', updateTime=1790231727008, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890060590346990, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890060594541295, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1609, endPage=1618, ext={EN=ArticleExt(id=1309892220422029802, articleId=1309892220233286121, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Ultra-short-term solar irradiance prediction model based on multi-source data and Transformer framework, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Suqiong Jiang¹, Peide Xu², Huaiyuan Wang³, authorsList=Suqiong Jiang, Peide Xu, Huaiyuan Wang, authorCompany=null, correspAuthors=Peide Xu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309892221135061483, articleId=1309892220233286121, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=基于多源数据和Transformer框架的超短期太阳辐照度预测模型, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

光伏发电的间歇性与波动性影响电网稳定，高精度预测太阳辐照度能为电力系统调度提供关键支撑。基于气象数据和天空图像的太阳辐照度预测方法已经被广泛研究，但是对于多尺度时序特征和天空图像特征的挖掘还十分有限，也没有充分利用特征间的相关性。文章提出了基于多源数据和Transformer框架的超短期太阳辐照度预测模型。首先，利用长短期记忆神经网络-Transformer学习多元气象序列的长短期时序变化特征；其次，利用卷积视觉Transformer对天空图像的全局信息和局部结构进行建模，提取深层次图像特征；最后，构建基于注意力机制的跨模态特征融合模块，利用图像特征表征时序特征，实现跨模态特征融合，由解码器输出预测结果。实验结果表明，与公认的先进方法相比，该方法的预测性能更优，与仅采用气象数据的情况相比，模型的归一化均方根误差平均降低了7.4%。模型通过挖掘多源数据（气象时序与天空图像）特征，并利用跨模态注意力机制有效挖掘其关联性，显著提升了预测精度。

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