A Short-Term Wind Power Prediction Model Based on the Improved Hippopotamus Optimization Algorithm and TCN-BiGRU-Self-Attention

A Short-Term Wind Power Prediction Model Based on the Improved Hippopotamus Optimization Algorithm and TCN-BiGRU-Self-Attention | 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 Article A Short-Term Wind Power Prediction Model Based on the Improved Hippopotamus Optimization Algorithm and TCN-BiGRU-Self-Attention Mengling Zhao, Liguo Wang, Jian Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9048301/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 7 You are reading this latest preprint version Abstract To address the intermittency and volatility of wind power generation, this paper proposes a hybrid forecasting model for short-term wind power prediction. It integrates a Temporal Convolutional Network (TCN), a Bidirectional Gated Recurrent Unit (BiGRU), a Self-Attention (SA) mechanism, and an Improved Hippopotamus Optimization algorithm (IHO). First, the TCN-BiGRU-SA forecasting framework is built. The TCN is employed to extract local temporal features in the wind power series, and the BiGRU is then utilized to further explore bidirectional long-term dependencies, and the self-attention mechanism is applied to adaptively weight the output features of BiGRU to highlight the contributions of the most salient features to the forecasting task. Moreover, the IHO algorithm is introduced to automatically optimize key hyperparameters such as the learning rate, the number of BiGRU neurons, and the L2 regularization coefficient, thereby alleviating the subjectivity inherent in manual parameter setting and enhancing the robustness of model performance. Compared with the standard HO, the proposed IHO incorporates a Sine Piecewise Map (SPM), lens opposition‑based learning, and a spiral search strategy to enhance global exploration and local exploitation. Finally, to comprehensively evaluate the performance of the proposed IHO-TCN-BiGRU-SA model, comparative experiments and ablation studies with multiple forecasting models were conducted. The empirical findings indicate that the proposed model delivers substantial gains in predictive accuracy while also exhibiting enhanced robustness and consistency. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Bidirectional Gated Recurrent Unit Improved Hippopotamus Optimization algorithm Temporal Convolutional Network Wind power prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Reviewers agreed at journal 25 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 25 Mar, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 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. 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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-9048301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611896901,"identity":"669f59ac-68b3-407f-ad52-ab1f3787d2b3","order_by":0,"name":"Mengling Zhao","email":"","orcid":"","institution":"Xi’an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mengling","middleName":"","lastName":"Zhao","suffix":""},{"id":611896902,"identity":"7207509b-b456-4427-b6c0-e20197221179","order_by":1,"name":"Liguo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYLCCBwZAgpn54IMPFRJy8kRpSQBpYW9LNpxxxsLYsIEoLSCC54yZNG9bRSLDAQKqDY6fPfwioaA2sV8iwUyCd55EAmMD88NHN/BpOZOXZpFgcDxx5oyEZAvJbRJ57AxsxsY5eLSYHcgxM0gwOJa44UbCwRuG2ySKGRt42KTxajn/BqYlsUEicY5EYsMBQlpu5Bg/SDCoSdxw5jCTxMEGIrTY33hjBgzkA8Yz29uYDRuOSRgbNhPwi2R/jvGHD3/qZPuZ+T8+/lNTJyfP3vzwMT4tQMAmwcBwGInPjF85WMkHBoY6wspGwSgYBaNg5AIAvuBTp2/wRpIAAAAASUVORK5CYII=","orcid":"","institution":"Xi’an University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Liguo","middleName":"","lastName":"Wang","suffix":""},{"id":611896903,"identity":"4e3e4e86-68ce-4718-ba9f-98dade33f00e","order_by":2,"name":"Jian Huang","email":"","orcid":"","institution":"Xi’an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-06 08:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9048301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9048301/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-49575-z","type":"published","date":"2026-04-18T15:59:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107350848,"identity":"f0719afc-d326-48c0-8421-1f20bcfaa680","added_by":"auto","created_at":"2026-04-20 16:05:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1735045,"visible":true,"origin":"","legend":"","description":"","filename":"AShortTermWindPowerPredictionModelBasedontheImprovedHippopotamusOptimizationAlgorithmandTCNBiGRUSelfAttention.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9048301/v1_covered_581d6239-a1ff-4fb0-8162-982acb0308b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Short-Term Wind Power Prediction Model Based on the Improved Hippopotamus Optimization Algorithm and TCN-BiGRU-Self-Attention","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bidirectional Gated Recurrent Unit, Improved Hippopotamus Optimization algorithm, Temporal Convolutional Network, Wind power prediction","lastPublishedDoi":"10.21203/rs.3.rs-9048301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9048301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the intermittency and volatility of wind power generation, this paper proposes a hybrid forecasting model for short-term wind power prediction. 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Compared with the standard HO, the proposed IHO incorporates a Sine Piecewise Map (SPM), lens opposition‑based learning, and a spiral search strategy to enhance global exploration and local exploitation. Finally, to comprehensively evaluate the performance of the proposed IHO-TCN-BiGRU-SA model, comparative experiments and ablation studies with multiple forecasting models were conducted. 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