Spatio-Temporal Feature Fusion via U-shaped Architecture for Accurate Wind Speed Prediction

Spatio-Temporal Feature Fusion via U-shaped Architecture for Accurate Wind Speed Prediction | 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 Spatio-Temporal Feature Fusion via U-shaped Architecture for Accurate Wind Speed Prediction Yue Gao, Zhongda Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7088881/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 Wind speed forecasting is a crucial support for ensuring the safe and stable operation of wind farms. However, due to the significant spatiotemporal variability of wind speed and the influence of complex meteorological factors, achieving highaccuracy forecasting remains a core challenge in the field of renewable energy generation. This study proposes a spatiotemporal feature fusion network based on a U-shaped architecture (U-STNet), which fully exploits the spatiotemporal dependencies within wind speed data by integrating spatial information from multiple wind turbines and long-range temporal dependencies across different periods. The model employs an embedding mechanism to project wind speed time series into a high-dimensional feature space, and utilizes an encoder-decoder Ushaped structure to perform encoding, reconstruction and multi-scale extract of high-dimensional features, effectively capturing the complex periodicity and seasonal variation patterns inherent in wind speed sequences. Experimental results on the SDWPF dataset demonstrate that the proposed method consistently outperforms existing mainstream forecasting models across multiple real-world wind speed datasets, significantly improving prediction accuracy. This approach provides an efficient and reliable spatiotemporal feature fusion modeling scheme for wind speed forecasting, with promising potential for broader application. Wind speed forecasting patiotemporal feature fusion U-shaped network Multi-scale feature extraction Full Text 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. 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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-7088881","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511356291,"identity":"b4e4e254-0f0e-4605-baa0-210d98c81034","order_by":0,"name":"Yue Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie3PMUvDQBTA8Xc8uCynDi6RQPMVEg4KQqRfJUG4yYrQD+BJIF2irunkt+h8etAugqtwDpWCk0OyVSli2s3BnG6C9xtuON6fxwNwnL8IPaXfPpLzwfy6WdTtB/VsiceyJVCRwr3icbVJ0JbsAedA71J4TPv7bLvXVuQggjOmTolMBSbv03AXgdTNyfeJr8ksqPynEYKaLYdXJi4Q8GAy7VijQfgseiEFuRjzYWlIm1Dc6UhCDX2fpZqUiDQ4LM3AmkS6PZ8pnVWU0gBWJrMmsWbZ80QKHjGG8aU0xwWSvPOW3sNcqUYmvSh8JYvV2hzdjPPbuuk6/wtSbF/50/mN9W+GHcdx/otPGw5SonCeVF8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0129-4018","institution":"Shenyang University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yue","middleName":"","lastName":"Gao","suffix":""},{"id":511356292,"identity":"7cbb8c5a-7cc2-4a91-995d-788457db7b9a","order_by":1,"name":"Zhongda Tian","email":"","orcid":"","institution":"Shenyang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhongda","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2025-07-10 04:39:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7088881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7088881/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98776411,"identity":"b6164f18-ed20-46b9-bdda-cdf1aa941be4","added_by":"auto","created_at":"2025-12-22 12:22:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1106069,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplatepaper6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7088881/v1_covered_0bca698f-578e-40a3-903b-bfd0de622b88.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSpatio-Temporal Feature Fusion via U-shaped Architecture for Accurate Wind Speed Prediction\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Wind speed forecasting, patiotemporal feature fusion U-shaped network, Multi-scale feature extraction","lastPublishedDoi":"10.21203/rs.3.rs-7088881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7088881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Wind speed forecasting is a crucial support for ensuring the safe and stable operation of wind farms. 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