A Hybrid Model for Ultra-short-term PV Prediction Using SOM Clustering and ECA

A Hybrid Model for Ultra-short-term PV Prediction Using SOM Clustering and ECA | 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 Hybrid Model for Ultra-short-term PV Prediction Using SOM Clustering and ECA Yixin Zhu, Ziyao Wang, Wei Zhang, Yufan Liu, Hao Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4573772/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Sep, 2024 Read the published version in Electrical Engineering → Version 1 posted 16 You are reading this latest preprint version Abstract For the grid to operate safely and steadily and for PV electricity to be connected on a broad scale, the precision of the ultra-short-term PV power prediction is crucial. A combination model of ultra-short-term PV prediction based on an attention mechanism is proposed to increase the prediction accuracy of PV output power under various weather circumstances. First, using a Pearson correlation coefficient analysis, important climatic variables that are closely associated with PV power generation are selected and normalized on a monthly basis. The Sky Condition Factor (SCF), a classification index, is then obtained by computing a weighted summation. This reduces the dimensionality of the input variables and eliminates seasonal influence on weather classification and the coupling interactions among various meteorological elements. Second, an unsupervised clustering of SCFs using a Self-Organizing Map (SOM) neural network is used to classify three types of weather. After that, CNN prediction models are built for each of the three types of weather. The Efficient Channel Attention (ECA) module is then added, allowing the model to focus on key feature information and increase prediction accuracy by adaptively assigning phase weights to each of the multiple channels of feature information that the CNN has extracted. Lastly, the efficacy of the suggested prediction model is verified by simulations run on historical observed data, which demonstrate an improvement in the prediction model's accuracy under various weather conditions when compared to the model without the ECA module. Pearson correlation coefficient Standardization SOM neural network ECA CNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Sep, 2024 Read the published version in Electrical Engineering → Version 1 posted Editorial decision: Revision requested 15 Jul, 2024 Reviews received at journal 13 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers invited by journal 14 Jun, 2024 Editor assigned by journal 13 Jun, 2024 Submission checks completed at journal 13 Jun, 2024 First submitted to journal 13 Jun, 2024 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-4573772","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319533368,"identity":"dd6b0e1f-715e-4844-b4de-83dd51b2d247","order_by":0,"name":"Yixin Zhu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Zhu","suffix":""},{"id":319533371,"identity":"ab805288-6d41-4d6c-9b48-45630c3335a8","order_by":1,"name":"Ziyao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNACAxs5fvbGxocfiNdSkGYs2XO42ViCeC0fDiduuJHeJsBDlJPOnzH88MEAqOXmwzYGCQY7Od0GQloOnDGWnGGQbjzzdmLbgwKGZGOzAwS0mB3sMZDmMbCW7bud2G4gwXAgcRtBLYd5jH/zGDAzNtw82CbBQ5SWYzxmQFucFSfcYCRSi/0ZtjLLGQagQE4EBrIBEX6R7D+8+caHP6CoPP7w4YcKOzmCWhgYOAyQOAY4lSED9gdEKRsFo2AUjIIRDAAprEXSHKShNAAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangnan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ziyao","middleName":"","lastName":"Wang","suffix":""},{"id":319533372,"identity":"5c9ea345-ddf6-45d0-885e-8ae04cde5929","order_by":2,"name":"Wei Zhang","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":319533375,"identity":"6eb04c6e-c66a-4fde-936e-a520e16885fb","order_by":3,"name":"Yufan Liu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yufan","middleName":"","lastName":"Liu","suffix":""},{"id":319533379,"identity":"12bc5a08-bc25-4e1b-9833-3099e2449880","order_by":4,"name":"Hao Wu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-06-13 05:56:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4573772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4573772/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00202-024-02710-3","type":"published","date":"2024-09-09T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64619357,"identity":"f08adabb-76a7-484b-a85c-9b6b53cdac65","added_by":"auto","created_at":"2024-09-16 16:14:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1663914,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4573772/v1_covered_14fc3da7-2f64-4523-938c-97aaea6e7b90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hybrid Model for Ultra-short-term PV Prediction Using SOM Clustering and ECA","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":"electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"elen","sideBox":"Learn more about [Electrical Engineering](http://link.springer.com/journal/202)","snPcode":"202","submissionUrl":"https://submission.nature.com/new-submission/202/3","title":"Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pearson correlation coefficient, Standardization, SOM neural network, ECA, CNN","lastPublishedDoi":"10.21203/rs.3.rs-4573772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4573772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"For the grid to operate safely and steadily and for PV electricity to be connected on a broad scale, the precision of the ultra-short-term PV power prediction is crucial. 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