Data denoising and deep learning prediction for the wind speed based on NOA optimization

Data denoising and deep learning prediction for the wind speed based on NOA optimization | 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 Data denoising and deep learning prediction for the wind speed based on NOA optimization Xinyi Xu, Shaojuan Ma, Cheng Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4699260/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 Accurate short-term wind speed prediction is of great significance for wind power generation. Due to the insufficient of traditional wind speed prediction methods to mine nonlinear features of information, an improved nonlinear time series prediction method is proposed by combining Variational Mode Decomposition (VMD) and Deep Learning (CNN-BiLSTM-AttNTS) with the Nutcracker Optimization Algorithm (NOA). Firstly, NOA is used to optimize VMD and CNN-BiLSTM, respectively. Secondly, we apply NOA-VMD to decompose the wind speed data into different Intrinsic Mode Functions(IMFs). Then, phase space reconstruction (PSR) is utilized to identify chaotic characteristics of the components. Finally, the NOA-CNN-BiLSTM-AttNTS model is built up to predict wind speed. Under the same hyperparameters and network structure settings, compared with traditional machine learning methods and state-of-the-art hybrid models, the results show that the R-squared of NOA-VMD-CNN-BiLSTM-AttNTS combination model proposed in this paper exceeds 90%, with good prediction accuracy and generalization performance. The research result can provide reference and guidance for short-term wind speed prediction. Wind speed prediction Chaotic time series Nutcracker Optimization Algorithm Variational Mode Decomposition CNN-BiLSTM-AttNTS Full Text Additional Declarations No competing interests reported. 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. 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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-4699260","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328437139,"identity":"2737575e-df0a-4db2-8762-7be06b7ae3d3","order_by":0,"name":"Xinyi Xu","email":"","orcid":"","institution":"North Minzu\nUniversity","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Xu","suffix":""},{"id":328437140,"identity":"b878ea76-a9a9-4a87-a42d-772bcf637dff","order_by":1,"name":"Shaojuan Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYFCCAwwMHyAsA+K1MM4gUQsDAzMPSVrkG08nfrbdsS2xgb15mwRDzR3CWgwOnN0snXvmdmIDz7EyCYZjz4jQwnB2g3RuG1CLRI6ZBGPDYSIc1nB2829LkBb5N0RqYThwdps0I9gWHiK1AP2yzbK37bZxG09asUXCMWIcNuPs5hs/227L9rMf3njjQw0xDpM4AKHZQEQCERoYGPgbiFI2CkbBKBgFIxkAAL6FPgNOxi6tAAAAAElFTkSuQmCC","orcid":"","institution":"North Minzu\nUniversity","correspondingAuthor":true,"prefix":"","firstName":"Shaojuan","middleName":"","lastName":"Ma","suffix":""},{"id":328437141,"identity":"c29e14bf-13de-4fbe-8da7-4854de6e2300","order_by":2,"name":"Cheng Huang","email":"","orcid":"","institution":"North Minzu\nUniversity","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-07-07 08:01:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4699260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4699260/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68028405,"identity":"200a0d87-6802-46a5-904e-3c1aa1e620e8","added_by":"auto","created_at":"2024-11-01 13:54:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1640148,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4699260/v1_covered_97ac8459-5b4f-4e04-aa27-10072144ab69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data denoising and deep learning prediction for the wind speed based on NOA optimization","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 prediction, Chaotic time series, Nutcracker Optimization Algorithm, Variational Mode Decomposition, CNN-BiLSTM-AttNTS","lastPublishedDoi":"10.21203/rs.3.rs-4699260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4699260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate short-term wind speed prediction is of great significance for wind power generation. Due to the insufficient of traditional wind speed prediction methods to mine nonlinear features of information, an improved nonlinear time series prediction method is proposed by combining Variational Mode Decomposition (VMD) and Deep Learning (CNN-BiLSTM-AttNTS) with the Nutcracker Optimization Algorithm (NOA). Firstly, NOA is used to optimize VMD and CNN-BiLSTM, respectively. Secondly, we apply NOA-VMD to decompose the wind speed data into different Intrinsic Mode Functions(IMFs). Then, phase space reconstruction (PSR) is utilized to identify chaotic characteristics of the components. Finally, the NOA-CNN-BiLSTM-AttNTS model is built up to predict wind speed. Under the same hyperparameters and network structure settings, compared with traditional machine learning methods and state-of-the-art hybrid models, the results show that the R-squared of NOA-VMD-CNN-BiLSTM-AttNTS combination model proposed in this paper exceeds 90%, with good prediction accuracy and generalization performance. 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