A Wind Power Forecasting Model Considering Peak Fluctuations

A Wind Power Forecasting Model Considering Peak Fluctuations | 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 A Wind Power Forecasting Model Considering Peak Fluctuations Shengjie YANG, Jie Tang, Lun Ye, Jiangang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6854491/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Wind power output sequences exhibit strong randomness and intermittency characteristics, traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise. A short-term wind power forecasting method based on an improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model's ability to capture regional segment features along the temporal dimension, enhancing the model's receptive field to address wind power fluctuation under varying environmental conditions; Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency-domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model's ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results. The experimental results validate the effectiveness and practicality of the proposed model. wind power forecasting Informer Frequency attention mechanism dilated convolution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 09 Jun, 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-6854491","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472220357,"identity":"a6a6b40c-cc92-428f-9b37-d629c940a479","order_by":0,"name":"Shengjie YANG","email":"","orcid":"","institution":"Hunan University of Technology and Business","correspondingAuthor":false,"prefix":"","firstName":"Shengjie","middleName":"","lastName":"YANG","suffix":""},{"id":472220358,"identity":"e80aceb8-14c7-4100-93d8-739933ea700f","order_by":1,"name":"Jie Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNmbmww8+VNTI8TMcPkCcFj72tjTDGWeOGUs2HksgTosczxkDad425sQNh88YEOkwiRwDAx42NmPJtjMfb7xhsJPTbSCoJa3ggQSPjBw/z9nNlnMYko3NDhDUkrzBwEACaMuMs9ukeRgOJG4jrCXBAIiAfrn/5hmRWniOGEgcSABqOXCGjUgtoEBuOAAM5IZjxpZzDIjwi3wz8+HHf/+Bo/LhjTcVdnIEtaAACR4iowZZC6k6RsEoGAWjYEQAABe5RDSJIkIBAAAAAElFTkSuQmCC","orcid":"","institution":"Hunan University of Technology and Business","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Tang","suffix":""},{"id":472220360,"identity":"a67575ec-a3af-4c10-b209-6df30f5b273a","order_by":2,"name":"Lun Ye","email":"","orcid":"","institution":"State Grid Hunan Electric Power Company Limited Economic \u0026 Technical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Lun","middleName":"","lastName":"Ye","suffix":""},{"id":472220361,"identity":"75a0c41d-eed6-4632-893e-5d2c2f44253a","order_by":3,"name":"Jiangang Liu","email":"","orcid":"","institution":"Hunan University of Technology and Business","correspondingAuthor":false,"prefix":"","firstName":"Jiangang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-09 12:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6854491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6854491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84907251,"identity":"2834b950-6e91-4ac3-a9b1-2e13c45c4855","added_by":"auto","created_at":"2025-06-18 16:10:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1420142,"visible":true,"origin":"","legend":"","description":"","filename":"AWindPowerForecastingModelConsideringPeakFluctuations.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6854491/v1_covered_73547c11-edb3-4600-be83-547f71a20fff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Wind Power Forecasting Model Considering Peak Fluctuations","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":false,"email":"","identity":"sustainable-energy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Sustainable Energy Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"wind power forecasting, Informer, Frequency attention mechanism, dilated convolution","lastPublishedDoi":"10.21203/rs.3.rs-6854491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6854491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWind power output sequences exhibit strong randomness and intermittency characteristics, traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise. A short-term wind power forecasting method based on an improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model's ability to capture regional segment features along the temporal dimension, enhancing the model's receptive field to address wind power fluctuation under varying environmental conditions; Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency-domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model's ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results. The experimental results validate the effectiveness and practicality of the proposed model.\u003c/p\u003e","manuscriptTitle":"A Wind Power Forecasting Model Considering Peak Fluctuations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 15:45:58","doi":"10.21203/rs.3.rs-6854491/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-26T05:06:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T15:23:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266947320849889974352782916512579924662","date":"2025-06-19T11:12:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T23:17:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T12:45:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T12:44:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sustainable Energy Research","date":"2025-06-09T12:28:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"sustainable-energy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Sustainable Energy Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"43a5d590-4b56-48f9-aba2-96374490a70a","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T05:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 15:45:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6854491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6854491","identity":"rs-6854491","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}