Smart Home Energy Management Using Deep Q-Learning Networks for Cost Reduction Under ToD Tariff

Smart Home Energy Management Using Deep Q-Learning Networks for Cost Reduction Under ToD Tariff | 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 Smart Home Energy Management Using Deep Q-Learning Networks for Cost Reduction Under ToD Tariff Ganesh Shirsat, Ankit Kumar Sharma, Satish Markad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8494831/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 Smart home energy management plays a vital role in reducing electricity costs and enhancing grid efficiency, especially under Time-of-Day (ToD) tariff schemes. This paper presents a comparative analysis between the classical Reinforcement Q-Learning (RLQ) and a Deep Q-Learning Network (DQLN) approach for intelligent appliance scheduling in residential settings. The objective is to minimize electricity costs while respecting user-defined operational constraints. Simulation results over a 24-hour horizon reveal that the proposed DQLN approach achieves a 17.76% reduction in energy cost. Moreover, DQLN demonstrates faster convergence, better load shaping, and higher adaptability to RTP fluctuations. It effectively shifts appliance loads to low-tariff periods, reduces peak demand, and maintains user comfort, thereby supporting demand-side management. The findings establish DQLN as a scalable and cost-effective solution for smart home automation. This study also outlines potential extensions involving renewable energy integration, multi-objective optimization, and edge deployment for real-time operation. Smart Home Energy Management Time-of-Day (ToD) tariff schemes Deep Q-Learning Network (DQLN) Reinforcement Learning Appliance Scheduling Demand Response Cost Minimization Smart Home. 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-8494831","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586583473,"identity":"9be4fae0-e9c5-466e-81af-4ab118a5d1a7","order_by":0,"name":"Ganesh Shirsat","email":"data:image/png;base64,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","orcid":"","institution":"Dr. Vithalrao Vikhe Patil College of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Ganesh","middleName":"","lastName":"Shirsat","suffix":""},{"id":586583474,"identity":"fdc4850f-7665-47a7-9194-6b6aa6768b12","order_by":1,"name":"Ankit Kumar Sharma","email":"","orcid":"","institution":"University of Engineering And Management","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"Kumar","lastName":"Sharma","suffix":""},{"id":586583475,"identity":"eca53dc8-2577-4884-b851-f912930820a9","order_by":2,"name":"Satish Markad","email":"","orcid":"","institution":"Dr. Vithalrao Vikhe Patil College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Satish","middleName":"","lastName":"Markad","suffix":""}],"badges":[],"createdAt":"2026-01-01 11:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8494831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8494831/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505544,"identity":"8a1b7029-130a-4dba-bd0f-44f3c5791ee8","added_by":"auto","created_at":"2026-02-26 13:31:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":875162,"visible":true,"origin":"","legend":"","description":"","filename":"UpdatedDiscoveryGANESHSHIRSATPaper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8494831/v1_covered_d0abca45-2620-4f9f-baf1-62ab005f63cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Smart Home Energy Management Using Deep Q-Learning Networks for Cost Reduction Under ToD Tariff","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":"Smart Home Energy Management, Time-of-Day (ToD) tariff schemes, Deep Q-Learning Network (DQLN), Reinforcement Learning, Appliance Scheduling, Demand Response, Cost Minimization, Smart Home.","lastPublishedDoi":"10.21203/rs.3.rs-8494831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8494831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmart home energy management plays a vital role in reducing electricity costs and enhancing grid efficiency, especially under Time-of-Day (ToD) tariff schemes. 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