Intelligent Power System Management Based on Machine Learning Technology

Intelligent Power System Management Based on Machine Learning Technology | 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 Method Article Intelligent Power System Management Based on Machine Learning Technology Jie Wu, Hanyuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8593825/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract This scholarly article delineates a novel approach for forecasting wind power load. The proposed methodology bifurcates the forecasting challenge into two principal sub-problems: the sequence modeling of temporal dynamic attributes and the nonlinear mapping of static characteristics. Through the integration of an attention mechanism, these sub-problems are synergistically amalgamated, culminating in a regression of the load value via a multilayer perceptron (MLP) network. This technique leverages the distinct advantages of various feature types, thereby encapsulating the temporal dependencies within series data while concurrently accentuating the representational power of static information, thereby offering a versatile and efficient solution for wind power load prediction. Furthermore, the present study delves into the deployment of the SNERDI power system intelligent management framework, which harnesses machine learning methodologies for the purposes of real-time data procurement and analysis, thereby enhancing power generation planning, equipment maintenance, and resource allocation. The integration of algorithms such as Support Vector Machines (SVM), Long Short-Term Memory networks (LSTM), and Reinforcement Learning has markedly augmented the system's predictive precision and fault anticipation capabilities. Empirical findings indicate that the LSTM model has achieved a daily load forecasting accuracy of 95%, representing an approximate 15 percentage point improvement over conventional methodologies. Additionally, the SVM model has demonstrated an equipment fault prediction accuracy exceeding 90%. The adoption of these advanced technologies has substantially bolstered the power system's safety, stability, resource allocation efficiency, and responsiveness to faults. Wind power load forecasting machine learning LSTM SVM intelligent management resource scheduling optimization equipment failure forecasting and system stability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 17 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 03 Feb, 2026 Editor invited by journal 30 Jan, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 24 Jan, 2026 First submitted to journal 24 Jan, 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-8593825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":584881012,"identity":"73889f49-a4fb-469f-aded-a344aef1a694","order_by":0,"name":"Jie Wu","email":"","orcid":"","institution":"Shanghai Nuclear Engineering Research \u0026 Design Institute CO.,LTD.","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wu","suffix":""},{"id":584881013,"identity":"de99d416-e231-40b6-8b89-6c6cd4d933d3","order_by":1,"name":"Hanyuan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBAC+/aGhAMfftjYMbY3EKnFgOfAw4Mze9KSmXsOEKtFIvHxYQ62w4ztMxKI1GLOkJxwmIHnMDPvzMcbbzDU2EQT1GLZcCzhcIFFOp/k7LRiC4ZjabkNBPUc7Ek4PIPHmtlwdo6ZBGPDYSK0HOb/cJiHjZlx/80zRGoxOMaQANTizNg4g4dILZI9DAngQGbsAfolgRi/8Ms/SP4AicrDG298qLEhwi/IjpRIIEU5RAupOkbBKBgFo2BkAAD9CUYeUxP3HAAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan College of Architectural Technology","correspondingAuthor":true,"prefix":"","firstName":"Hanyuan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-13 15:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8593825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8593825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102295103,"identity":"a7c660e8-f770-42e5-a6a7-cc9f28d4e76f","added_by":"auto","created_at":"2026-02-10 10:08:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589730,"visible":true,"origin":"","legend":"","description":"","filename":"IntelligentPowerSystemManagementBasedonMachineLearningTechnology.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8593825/v1_covered_e9d48799-3276-46d9-aeef-d378e174d680.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent Power System Management Based on Machine Learning Technology","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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wind power load forecasting, machine learning, LSTM, SVM, intelligent management, resource scheduling optimization, equipment failure forecasting, and system stability","lastPublishedDoi":"10.21203/rs.3.rs-8593825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8593825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis scholarly article delineates a novel approach for forecasting wind power load. 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