Industrial Big Data-Enabled Deep Learning: Integrating AI-Driven Adaptive Attention with LSTM for Advanced Solar Photovoltaic Power Forecasting

Industrial Big Data-Enabled Deep Learning: Integrating AI-Driven Adaptive Attention with LSTM for Advanced Solar Photovoltaic Power Forecasting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Industrial Big Data-Enabled Deep Learning: Integrating AI-Driven Adaptive Attention with LSTM for Advanced Solar Photovoltaic Power Forecasting Shuai Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5076329/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The escalating global deployment of Solar Photovoltaic (PV) systems highlights an urgent demand for sophisticated power output forecasting methods that leverage the vast datasets characteristic of industrial applications. Precise forecasting is essential for maintaining grid stability, optimizing energy distribution, and balancing the supply-demand equation, particularly given the unpredictable nature of solar energy sources. Long Short-Term Memory (LSTM) networks, renowned for their proficiency in capturing long-term dependencies within time-series data, have emerged as a leading solution to this forecasting challenge. However, the static attention mechanisms in traditional LSTM models are ill-equipped to handle the complex dynamics of solar power data. This paper introduces an innovative LSTM model that integrates an AI-driven adaptive attention mechanism, designed to harness the power of industrial big data for enhanced forecasting. The model dynamically identifies and emphasizes relevant temporal features, thereby increasing its responsiveness to real-time data fluctuations. Our approach, after extensive validation using a diverse set of benchmark solar PV datasets, demonstrates a remarkable improvement in forecasting accuracy over conventional LSTM models. The adaptive attention mechanism confers a twofold benefit: it enhances the model's adaptability by adjusting its focus in accordance with shifting data patterns, and it promotes interpretability by clarifying which temporal features are considered most influential by the model at any given moment. Ultimately, the integration of adaptive attention with LSTM establishes a new precedent in solar PV power forecasting and suggests broader applications across the renewable energy sector, showcasing the model's adaptability and wide-ranging potential. Industrial Big Data Deep Learning Adaptive Attention-Enhanced LSTM Precision Forecasting Solar Photovoltaic Systems Power Output Forecasting Grid Stability Energy Optimization Supply-Demand Ratio Intermittent Solar Energy Sources Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5076329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":353005150,"identity":"80b13fdc-3a73-4dd3-b2a5-f843fbe72820","order_by":0,"name":"Shuai Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFACxgYGBgMbHjb2xsaHH4jXUpAmw8dzuNlYgnibPhy2kZNIbxPgIUaxfHtz64YPBsw8bJIP2xgkGOzkdBsIOavnYNvNGQZsPGzSiW0PChiSjc0OENDCLJHYdpvHgAekpd1AguFA4jZCWtjkH4K0SAAddrBNgocYLTwSjCAtBjxsQAZxWiR4EkF+SeBh40kEBrIBEX6Rbz/+7MaHP//tgYyHDz9U2MkR1IIGDEhTPgpGwSgYBaMABwAAEU88p3Ym/cQAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-09-12 08:59:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5076329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5076329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64423677,"identity":"e3283c6a-3f8f-43b3-95f1-c9027208305a","added_by":"auto","created_at":"2024-09-13 03:31:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":334605,"visible":true,"origin":"","legend":"","description":"","filename":"DeepLearningDrivenSolarPhotovoltaicPowerForecastingwithIndustrialBigDataandAdaptiveAttentionEnhancedLSTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5076329/v1_covered_e3eedc41-3bd8-465e-a1d6-2034a7e843b6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIndustrial Big Data-Enabled Deep Learning: Integrating AI-Driven Adaptive Attention with LSTM for Advanced Solar Photovoltaic Power Forecasting\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Industrial Big Data, Deep Learning, Adaptive Attention-Enhanced LSTM, Precision Forecasting, Solar Photovoltaic Systems, Power Output Forecasting, Grid Stability, Energy Optimization, Supply-Demand Ratio, Intermittent Solar Energy Sources","lastPublishedDoi":"10.21203/rs.3.rs-5076329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5076329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating global deployment of Solar Photovoltaic (PV) systems highlights an urgent demand for sophisticated power output forecasting methods that leverage the vast datasets characteristic of industrial applications. 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