Using Linear Regression Analysis to Predict Energy Consumption

Using Linear Regression Analysis to Predict Energy Consumption | 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 Using Linear Regression Analysis to Predict Energy Consumption Phan Dao Dao Phan, Minh Anh Nguyen Nguyen Minh Anh, Ba Hung HUng Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590592/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 study explores the utilization of linear regression analysis to predict energy consumption in practical applications. The methodology involves setting up a linear regression model and applying it to real-world scenarios to demonstrate its effectiveness. Specifically, the research includes three case studies: predicting the future energy consumption of a supermarket in the UK, analyzing the energy consumption patterns of the Puerto Princesa Distribution System, and building a predictive model using data from Internet of Things (IoT) devices. The application of linear regression in these examples illustrates how accurate predictions can be achieved. Additionally, the study presents an improved energy prediction model, showcasing enhancements in predictive accuracy. The findings indicate that linear regression is a valuable tool for energy consumption forecasting, providing insights that can aid in better energy management and planning. Full Text Additional Declarations No competing interests reported. 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-4590592","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318980187,"identity":"c065d5a4-ec52-4476-aff1-1115b3b2b555","order_by":0,"name":"Phan Dao Dao Phan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDCCAwzsPxgYJOTA7AdEamGQYGCwMAazE0jQUpHYAOIQpYXv+OEDBj9qJNLnhx1+CLTFTk63gYAWyTNpCYk9xyRyN95OMwBqSTY2O0BAi8GBHIMDvA1ALbMTQFoOJG4jqOX8G8PGvw0S6Yaz0z8QqeVGjjEz0JYEeekcIm2RvPEsjVnmmIThBumcggMJBkT4he988jHGNzV18vKz0zd/+FBhJ0dQC8KFYJUGxCoHAfkGUlSPglEwCkbBiAIAHnlIT4j6VA0AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Phan","middleName":"Dao Dao","lastName":"Phan","suffix":""},{"id":318980188,"identity":"4369c289-85bf-4159-a6a6-c41ad438159a","order_by":1,"name":"Minh Anh Nguyen Nguyen Minh Anh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Minh","middleName":"Anh Nguyen Nguyen Minh","lastName":"Anh","suffix":""},{"id":318980189,"identity":"fdb90afd-f5b1-4ab5-b4aa-723a1544f23a","order_by":2,"name":"Ba Hung HUng Nguyen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ba","middleName":"Hung HUng","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2024-06-16 17:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4590592/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4590592/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63080035,"identity":"79e07201-415a-4b9a-99df-3f35143a42ff","added_by":"auto","created_at":"2024-08-23 00:31:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277164,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4590592/v1_covered_3af1742b-9693-4654-9f91-ffc3d83a7b3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eUsing Linear Regression Analysis to Predict Energy Consumption\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4590592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4590592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study explores the utilization of linear regression analysis to predict energy consumption in practical applications. 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