Energy Conservation in Smart Grids: Predictive Analytics via Customer Behavior for Adaptive Load Management

Energy Conservation in Smart Grids: Predictive Analytics via Customer Behavior for Adaptive Load Management | 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 Energy Conservation in Smart Grids: Predictive Analytics via Customer Behavior for Adaptive Load Management Rayhan Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8600831/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 This paper introduces advanced analytical models for optimizing electricity distribution and fostering energy conser- vation within contemporary smart grid infrastructures. We delve into methods for deriving precise customer consumption patterns, which are critical for accurate short-term load forecasting. The proposed methodology leverages probabilistic simulations to anticipate future energy demands, providing a robust framework for grid operators. Comparative assessments against existing forecasting techniques affirm the high accuracy and valuable properties of our approach. This continuous evaluation serves as a vital feedback mechanism, enabling adaptive load management strategies that enhance overall power efficiency and contribute significantly to energy conservation efforts in an increasingly bidirectional electrical landscape. Autometic meter reading confidence interval Full Text Additional Declarations The authors declare no competing interests. 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. 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-8600831","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574480922,"identity":"23c26399-f7b3-4799-9847-64b7a1b3115e","order_by":0,"name":"Rayhan Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYHACNiBOSGBgYD9gIPnHBshhbDxApBaehALLhjSQlgZitTAYfKhsOAwWwqvFvP2M2YOfO9Ly+Gc3JG64ueO83dr2w0BbamyicWmROZNjbth7JqdY4s7Bw4Yzz9xO3nYmEajlWFpuAw4tEgw5ZhK8bRWJDTcS0owl2G4nmx0AamFsOIxbC/8bM8m/QC3zbySY//7Ddi7Z7PxDAlokcsykedtyEjfcSDAwkGw7YGd2g5AtEs/KpGXb0hI33shJMJA4k5xgdgNoSwI+v/Anb5N825acOO9G+gEDiQo7e7Pz6Q8ffKixwamFgYHDAIWbCFaZgFM5CLA/QOHa41U8CkbBKBgFIxIAABTwaj8n7EDOAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Rayhan","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2026-01-14 10:51:54","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8600831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8600831/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100595098,"identity":"bdbe01ae-565f-4f69-9d89-57ce4d7e250c","added_by":"auto","created_at":"2026-01-19 13:47:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":192793,"visible":true,"origin":"","legend":"","description":"","filename":"ENERGY1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8600831/v1_covered_7c04e3c5-c393-4cad-8f0d-f6596249f44c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnergy Conservation in Smart Grids: Predictive Analytics via Customer Behavior for Adaptive Load Management\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Autometic meter reading, confidence interval","lastPublishedDoi":"10.21203/rs.3.rs-8600831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8600831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces advanced analytical models for optimizing electricity distribution and fostering energy conser- vation within contemporary smart grid infrastructures. 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