Enhanced Transformer-EA Evolutionary Algorithm Fusion Method for High-Precision Completion of Power Load Time Series Data

Enhanced Transformer-EA Evolutionary Algorithm Fusion Method for High-Precision Completion of Power Load Time Series Data | 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 Enhanced Transformer-EA Evolutionary Algorithm Fusion Method for High-Precision Completion of Power Load Time Series Data Chao Li, Yiyang meng, Ji Li, Yuzheng Liu, Changlong Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8877227/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Aiming at the problem of missing power time-series data in smart grids caused by equipment failures, communication interruptions, etc., this paper proposes a high-precision completion method for power load time-series data based on the deep integration of enhanced Transformer and evolutionary algorithm (EA). This method optimizes the time-series attention mechanism of the Transformer model through a dual-baseline pre-filling strategy, combined with the periodic characteristics and global statistical information of power loads, and introduces a dynamically decaying EA evolutionary algorithm to finely repair missing data. To further improve the completion accuracy, a multi-dimensional weighted loss function is designed in this paper to emphasize the accuracy optimization during peak hours, and physical compliance constraints are used to ensure that the completion results fall within the reasonable range of power loads. Experimental results show that the proposed method exhibits superior completion performance under different missing mechanisms (MCAR, MAR, MNAR) and missing rates (10%, 20%, 30%). Compared with traditional time-series models and basic Transformer, the enhanced Transformer-EA model has significant improvements in core indicators such as R², MAPE, and RMSE. This method provides an efficient solution for power load data missing completion and has broad application prospects and practical value Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing power load evolutionary algorithm missing data imputation deep learning Transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Feb, 2026 Editor invited by journal 25 Feb, 2026 Editor assigned by journal 17 Feb, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 14 Feb, 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. 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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-8877227","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597908681,"identity":"ce3398fb-2d27-4df6-b57c-6934a00c4c2e","order_by":0,"name":"Chao Li","email":"","orcid":"","institution":"天津理工大学","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Li","suffix":""},{"id":597908682,"identity":"95b3a04c-2939-4f38-a2e8-f37b30c00ecc","order_by":1,"name":"Yiyang meng","email":"","orcid":"","institution":"天津理工大学","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"meng","suffix":""},{"id":597908683,"identity":"ad8ffc55-1591-4841-b8f2-4f9d32bd3744","order_by":2,"name":"Ji Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RMQrCMBSA4SeFdKl2rUg9QyFQHYpnSSh0dHHpWKh0dO8tAi6OkUBdcgA3dencG2hTHNShyeiQf3qEfCEkADbbHzZzgHCSB0sfgKiFSaEjSJFOJnhe9YQbEXVwvc9o1PSTGXHhIaaFIPhWtIsOkpBxp73rLiamJ7GNG8gCDhlmHK0iPZFi9yaCMu6hQE8qQY/VQJ5m5FxXGWVoINyMDI8cNCRdyyjFtUDxKPF9mXbDV5aSXvN8Ex4uZTtKADzyMaincsb397n8d7DZbDbbdy8PXEx/M5YyRAAAAABJRU5ErkJggg==","orcid":"","institution":"天津理工大学","correspondingAuthor":true,"prefix":"","firstName":"Ji","middleName":"","lastName":"Li","suffix":""},{"id":597908684,"identity":"aae7bd53-24f5-464a-8bdb-ece1f8eb3ad1","order_by":3,"name":"Yuzheng Liu","email":"","orcid":"","institution":"天津理工大学","correspondingAuthor":false,"prefix":"","firstName":"Yuzheng","middleName":"","lastName":"Liu","suffix":""},{"id":597908685,"identity":"4d4bd2ac-b275-4ac0-a06d-737509d29c7b","order_by":4,"name":"Changlong Lv","email":"","orcid":"","institution":"天津理工大学","correspondingAuthor":false,"prefix":"","firstName":"Changlong","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2026-02-14 05:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8877227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8877227/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103897702,"identity":"9eb7bd6d-7dbe-49a2-a7c0-43b39af33851","added_by":"auto","created_at":"2026-03-04 09:19:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":814849,"visible":true,"origin":"","legend":"","description":"","filename":"2.4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8877227/v1_covered_43f49fb8-0879-4279-8e60-ac1aa6b93f0f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Transformer-EA Evolutionary Algorithm Fusion Method for High-Precision Completion of Power Load Time Series Data","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":"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":"power load, evolutionary algorithm, missing data imputation, deep learning, Transformer","lastPublishedDoi":"10.21203/rs.3.rs-8877227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8877227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problem of missing power time-series data in smart grids caused by equipment failures, communication interruptions, etc., this paper proposes a high-precision completion method for power load time-series data based on the deep integration of enhanced Transformer and evolutionary algorithm (EA). This method optimizes the time-series attention mechanism of the Transformer model through a dual-baseline pre-filling strategy, combined with the periodic characteristics and global statistical information of power loads, and introduces a dynamically decaying EA evolutionary algorithm to finely repair missing data. To further improve the completion accuracy, a multi-dimensional weighted loss function is designed in this paper to emphasize the accuracy optimization during peak hours, and physical compliance constraints are used to ensure that the completion results fall within the reasonable range of power loads. Experimental results show that the proposed method exhibits superior completion performance under different missing mechanisms (MCAR, MAR, MNAR) and missing rates (10%, 20%, 30%). Compared with traditional time-series models and basic Transformer, the enhanced Transformer-EA model has significant improvements in core indicators such as R\u0026sup2;, MAPE, and RMSE. 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