Hybrid photovoltaic power prediction method based on subsystem staged optimization and sample-level fine classification

Hybrid photovoltaic power prediction method based on subsystem staged optimization and sample-level fine classification | 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 Hybrid photovoltaic power prediction method based on subsystem staged optimization and sample-level fine classification Xinjie Shi, Jianzhou Wang, Xiayan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8291457/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Accurately forecasting short-term photovoltaic power generation is crucial for ensuring power system stability. However, traditional methods, due to the limitations of "fixed-stage sub-model training" and "coarse weather classification," struggle to cope with the high randomness of photovoltaic power generation, resulting in insufficient prediction accuracy and practicality. This paper proposes a hybrid photovoltaic power forecasting system that incorporates fuzzy time series preprocessing, subsystem selection, and model optimization modules. Its key breakthroughs lie in two key strategies: First, a "full-stage performance testing - optimal stage selection" mechanism is designed to screen five seed models from linear, machine learning, and pure attention models. The optimal output stage for each sub-model is determined through iterative verification, addressing the performance loss associated with fixed training. Second, a sample-level fine classification system is constructed, eliminating coarse classification and incorporating features such as irradiance and temperature to achieve one-to-one "model-to-sample" adaptation, mitigating the influence of individual differences. The system utilizes fuzzy preprocessing to improve data quality, and employs an enhanced whale optimization algorithm (EWOA) combined with simulated annealing (SA) to avoid local optima. Experiments on the Safi-Morocco three-dataset system show that the system significantly improves prediction accuracy and stability through the synergy of subsystem optimization and fine classification, providing a new path for high-fluctuation energy power prediction. photovoltaic power phased optimization fine classification fuzzy time series Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 06 Dec, 2025 Submission checks completed at journal 06 Dec, 2025 First submitted to journal 05 Dec, 2025 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-8291457","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561968316,"identity":"5b0a0fb9-6096-48da-b691-3f98d33d89b6","order_by":0,"name":"Xinjie Shi","email":"","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinjie","middleName":"","lastName":"Shi","suffix":""},{"id":561968318,"identity":"bf5db4e3-852e-4787-bda9-58e730acf75a","order_by":1,"name":"Jianzhou Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxgYwZQPlshGvJQ2ImYnUAgWHSdDC3H/G8HPBr/PRBufPH2D4UHaYQbe9gZDDzhhLz+y7nbvhRjID44xzhxnMzhwgoKWxd4M0bw9ICzMDM28bUMuNBAJamnk3/+btOZe74fxhBua/RGlp490mzfPjQO6GA8kMzIxEaenh/2bN25CcO/NGssHBnnPpPAT9Yth/LPk2zx+73L7zBx8++FFmLWd2vIGAlgaw6yAckPE8+NUDgTyY/ENQ3SgYBaNgFIxkAAA8QUnWvz9t1AAAAABJRU5ErkJggg==","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jianzhou","middleName":"","lastName":"Wang","suffix":""},{"id":561968320,"identity":"e5e4eb8a-7527-4b4d-b152-fe97deba45a9","order_by":2,"name":"Xiayan Liu","email":"","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiayan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-12-06 02:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8291457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8291457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99308748,"identity":"51133399-d0ef-4167-b44a-281d70f954c1","added_by":"auto","created_at":"2025-12-31 16:09:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1993975,"visible":true,"origin":"","legend":"","description":"","filename":"0903manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8291457/v1_covered_634b1666-baa7-4478-a114-d1d80ea90e13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid photovoltaic power prediction method based on subsystem staged optimization and sample-level fine classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"photovoltaic power, phased optimization, fine classification, fuzzy time series ","lastPublishedDoi":"10.21203/rs.3.rs-8291457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8291457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurately forecasting short-term photovoltaic power generation is crucial for ensuring power system stability. 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Second, a sample-level fine classification system is constructed, eliminating coarse classification and incorporating features such as irradiance and temperature to achieve one-to-one \"model-to-sample\" adaptation, mitigating the influence of individual differences. The system utilizes fuzzy preprocessing to improve data quality, and employs an enhanced whale optimization algorithm (EWOA) combined with simulated annealing (SA) to avoid local optima. 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