Article(id=1309639383788122955, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, 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=1724169600000, receivedDateStr=2024-08-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171961018, onlineDateStr=2026-09-23, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171961018, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171961018, creator=13701087609, updateTime=1790171961018, updator=13701087609, issue=Issue{id=1309639371255542472, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='11', pageStart='1421', pageEnd='1562', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171958030, creator='13701087609', updateTime=1790231751141, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890161824063505, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890161824063506, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1458, endPage=1468, ext={EN=ArticleExt(id=1309639383993643852, articleId=1309639383788122955, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=DBO-CNN-BiLSTM-AM short-term photovoltaic power generation prediction based on feature optimization, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

With the gradual increase of photovoltaic installed capacity, the stable and safe operation of power system is facing challenges. By improving the prediction accuracy of photovoltaic power generation, the operation stability and safety of power system can be improved. Therefore, this paper proposes a DBO -CNN -BiLSTM -AM short -term photovoltaic power generation prediction method based on feature optimization. Firstly, Variance Inflation Factor (VIF) and Extremely Randomized Trees (ERT) are used to calculate and select the features with low VIF value and high importance score as input. Secondly, a CNN -BiLSTM -AM prediction model is established by using Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory (BiLSTM) and Attention Mechanism (AM) with dynamic adjustment ability of feature weights. Finally, the Dung Beetle Optimization algorithm (DBO) is used to perform global optimization of hyperparameters to maximize the prediction accuracy. The experimental results show that the prediction accuracy of the short-term photovoltaic power prediction method proposed in this paper is significantly higher than that of the traditional method, which provides a good basis for optimizing power dis-patching and stable operation of power system.

, authors=Li Du^{1, 2}, Zhenhua Li^{1, 2}, Zhenxing Li¹, Wei Wei³, Yanchun Xu¹, authorsList=Li Du, Zhenhua Li, Zhenxing Li, Wei Wei, Yanchun Xu, authorCompany=null, correspAuthors=Zhenhua Li, 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=1309639384207553357, articleId=1309639383788122955, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=基于特征优选的DBO-CNN-BiLSTM-AM短期光伏发电预测, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

随着光伏装机容量逐渐增大，电力系统稳定、安全运行面临挑战，通过提高光伏发电预测精度，可以提高电力系统运行的稳定性和安全性。为此，文章提出一种基于特征优选的DBO-CNN-BiLSTM-AM短期光伏发电预测方法。首先，利用方差膨胀因子（Variance Inflation Factor, VIF）和极端随机树（Extremely Randomized Trees, ERT）计算并选取VIF值低且重要性得分高的特征作为输入；其次，采用卷积神经网络（Convolutional Neural Network, CNN）、双向长短期神经网络（Bi-directional Long Short Term Memory, BiLSTM）和具有特征权重动态调整能力的注意力机制（Attention Mechanism, AM）建立CNN-BiLSTM-AM预测模型；最后，利用蜣螂优化算法（Dung Beetle Optimization algorithm, DBO）进行超参数全局寻优，以最大化预测精度。实验结果表明，文章提出的短期光伏发电功率预测方法预测精度明显高于传统方法，为优化电力调度和电力系统稳定运行提供了良好的基础。

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