Two Step Wind Speed Prediction Based on a U-shaped RBiLSTM Hybrid Model

Two Step Wind Speed Prediction Based on a U-shaped RBiLSTM Hybrid Model | 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 Two Step Wind Speed Prediction Based on a U-shaped RBiLSTM Hybrid Model Yue Gao, Zhongda Tian This is a preprint; it has not been peer reviewed by a journal.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-6745960/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Accurate wind speed prediction can improve energy utilization, reduce energy waste, and save motor maintenance costs. A novel wind speed prediction model, the BEGWO-URBiLSTM model, is proposed for ultra-short-term 2 step wind speed prediction. Using the Empirical Mode Decomposition (EMD) method, IMF components corresponding to different frequency domains are extracted from the raw wind speed data, and then a U-shaped Reinforced Bidirectional Long ShortTerm Memory(URBiLSTM) neural network model is proposed for time series prediction of these extracted IMF components. The U-shape structure equips the model with encoding and decoding capabilities, which helps capture features with long-term dependencies. The peekhole structure in RBiLSTM enables the model to acquire more information during time series processing, while the bidirectional architecture allows it to simultaneously capture information from both the past and the future. We also propose a population behavior-enhanced Gray Wolf Optimizer (BEGWO) that incorporates metaheuristic adaptive improvements to the standard GWO. This approach dynamically balances the algorithm’s global exploration and local search capabilities, thereby enhancing its convergence speed. BEGWO was used to optimize the hyperparameters in URBiLSTM, and the accurate selection of hyperparameters improved the prediction accuracy of the model. Experimental results show that the proposed model outperforms other models in terms of prediction accuracy and reliability when applied to complex wind speed time series forecasting. Time Series Prediction Long Short-Term Memory Neural Network Metaheuristic Swarm Intelligence Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Jun, 2025 Editor assigned by journal 27 May, 2025 First submitted to journal 25 May, 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. 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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-6745960","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468381501,"identity":"3dae4064-cf1d-4d00-81be-e6268dcd41ea","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":468381502,"identity":"fe6fbc76-9407-4d9c-a320-8471f97d609c","order_by":1,"name":"Zhongda Tian","email":"","orcid":"","institution":"Shenyang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhongda","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2025-05-26 00:22:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6745960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6745960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84331165,"identity":"e71686ae-e1fc-4b04-8046-e9fd617c2cf7","added_by":"auto","created_at":"2025-06-10 16:03:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1036043,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6745960/v1_covered_dd48d4bb-c193-4464-b78b-81a62f16230d.pdf"}],"financialInterests":"","formattedTitle":"Two Step Wind Speed Prediction Based on a U-shaped RBiLSTM Hybrid Model","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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Time Series Prediction, Long Short-Term Memory Neural Network, Metaheuristic, Swarm Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-6745960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6745960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate wind speed prediction can improve energy utilization, reduce energy waste, and save motor maintenance costs. 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This approach dynamically balances the algorithm’s global exploration and local search capabilities, thereby enhancing its convergence speed. BEGWO was used to optimize the hyperparameters in URBiLSTM, and the accurate selection of hyperparameters improved the prediction accuracy of the model. 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