Cost-Optimal Coordination for Peak Demand Reduction in Saudi Residential Buildings Using Physics-Informed Deep Reinforcement Learning

Cost-Optimal Coordination for Peak Demand Reduction in Saudi Residential Buildings Using Physics-Informed Deep Reinforcement Learning | 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 Cost-Optimal Coordination for Peak Demand Reduction in Saudi Residential Buildings Using Physics-Informed Deep Reinforcement Learning Hamzah Faraj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9127190/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract When multiple On/Off split air-conditioning units in Saudi residential buildings ac-tivate simultaneously, the resulting peak demand spike stresses the electrical gridand inflates monthly bills under the kingdom’s two-tier tariff (0.18 SAR/kWh ≤6,000 kWh; 0.30 SAR/kWh above). This paper proposes a Physics-Informed ProximalPolicy Optimization (PI-PPO) framework that learns a stationary scheduling policy—applicable over an infinite time horizon without re-solving any optimization—to co-ordinate 18,500 BTU On/Off split units (1.8 kW input, EER 10.25) across multiplezones. Each zone is abstracted as a scheduling task with formally analyzed minimumutilization and feasibility conditions. The model incorporates inter-zone thermal cou-pling, enabling the scheduler to exploit thermal buffering through shared walls. PI-PPO embeds heat balance equations directly into the reinforcement learning reward,yielding a controller that maintains thermal comfort within the specified bounds at alltimes—a guarantee absent from standard deep reinforcement learning methods. Wefurther show that extending the comfort range by ±1◦C (from 23–25◦C to 22–26◦C)reduces each zone’s minimum utilization by 36.9%. Simulations using EnergyPluswith Jeddah weather data across four months (January, April, July, October) showthat PI-PPO reduces peak demand by 40–60% and July cost by 22.5% for a 5-zonevilla, rising to 47.0% for a 20-zone compound with comfort extension. Ablation stud-ies attribute 6.0 percentage points to physics-informed shaping, 4.5 pp to tiered-tariffawareness, 2.0 pp to inter-zone coupling, and 14.0 pp to comfort extension. peak demand reduction deep reinforcement learning physics-informed learning HVAC scheduling thermal comfort Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 15 Mar, 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-9127190","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613341327,"identity":"8c08269e-99a8-446c-9890-8c61297ec4fb","order_by":0,"name":"Hamzah Faraj","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBAC9gYeMJ3AD6bYiNDCcwCqRbKNZC0Gx4jWwsB78NONP3Z5xvd7DBg+lB1mkG8/QEgLX7J0Dk9ysdkxHgPGGecOMxicScCvxZ6Bx0A6R4I5cRtQCzNvG1ALAwEtPAw8xr9zDOoTN7cBtfwFapHvf0BQi5l0TsLhxA1sQC2MQC0MNwjZwsxjZp1z4HjijGNpBQd7zqXzGNwgZAt7j/HtnD/Vif3Nhzc++FFmLSffT8AWBmYk9gGwS0fBKBgFo2AUUA4A2EM86dUB0jsAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hamzah","middleName":"","lastName":"Faraj","suffix":""}],"badges":[],"createdAt":"2026-03-15 08:23:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9127190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9127190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105702961,"identity":"74008f10-ae2d-40d2-bebd-e3c67511c538","added_by":"auto","created_at":"2026-03-30 06:27:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":300696,"visible":true,"origin":"","legend":"","description":"","filename":"CostOptimalCoordinationforPeakDemandReductioninSaudiResidentialBuildingsUsingPhysicsInformedDeepReinforcementLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9127190/v1_covered_6b6ad528-866a-4035-89b0-dab2056a3886.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-Optimal Coordination for Peak Demand Reduction in Saudi Residential Buildings Using Physics-Informed Deep Reinforcement Learning","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":"journal-of-king-saud-university-engineering-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University – Engineering Sciences](https://link.springer.com/journal/44444)","snPcode":"44444","submissionUrl":"https://submission.springernature.com/new-submission/44444/3","title":"Journal of King Saud University – Engineering Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"peak demand reduction, deep reinforcement learning, physics-informed learning, HVAC scheduling, thermal comfort","lastPublishedDoi":"10.21203/rs.3.rs-9127190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9127190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"When multiple On/Off split air-conditioning units in Saudi residential buildings ac-tivate simultaneously, the resulting peak demand spike stresses the electrical gridand inflates monthly bills under the kingdom’s two-tier tariff (0.18 SAR/kWh ≤6,000 kWh; 0.30 SAR/kWh above). This paper proposes a Physics-Informed ProximalPolicy Optimization (PI-PPO) framework that learns a stationary scheduling policy—applicable over an infinite time horizon without re-solving any optimization—to co-ordinate 18,500 BTU On/Off split units (1.8 kW input, EER 10.25) across multiplezones. Each zone is abstracted as a scheduling task with formally analyzed minimumutilization and feasibility conditions. The model incorporates inter-zone thermal cou-pling, enabling the scheduler to exploit thermal buffering through shared walls. PI-PPO embeds heat balance equations directly into the reinforcement learning reward,yielding a controller that maintains thermal comfort within the specified bounds at alltimes—a guarantee absent from standard deep reinforcement learning methods. Wefurther show that extending the comfort range by ±1◦C (from 23–25◦C to 22–26◦C)reduces each zone’s minimum utilization by 36.9%. Simulations using EnergyPluswith Jeddah weather data across four months (January, April, July, October) showthat PI-PPO reduces peak demand by 40–60% and July cost by 22.5% for a 5-zonevilla, rising to 47.0% for a 20-zone compound with comfort extension. Ablation stud-ies attribute 6.0 percentage points to physics-informed shaping, 4.5 pp to tiered-tariffawareness, 2.0 pp to inter-zone coupling, and 14.0 pp to comfort extension.","manuscriptTitle":"Cost-Optimal Coordination for Peak Demand Reduction in Saudi Residential Buildings Using Physics-Informed Deep Reinforcement Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 06:25:13","doi":"10.21203/rs.3.rs-9127190/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-15T15:16:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T16:16:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168511656334234083804508172144010688880","date":"2026-03-27T13:30:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338173944963493310352963353457812011879","date":"2026-03-27T08:42:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313015270396363996156492941557251023344","date":"2026-03-27T02:29:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-27T02:27:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T10:23:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T11:04:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of King Saud University – Engineering Sciences","date":"2026-03-15T08:13:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-engineering-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University – Engineering Sciences](https://link.springer.com/journal/44444)","snPcode":"44444","submissionUrl":"https://submission.springernature.com/new-submission/44444/3","title":"Journal of King Saud University – Engineering Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b3ec71b-02db-4dc2-85f0-4506b6e35b9f","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T06:25:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 06:25:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9127190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9127190","identity":"rs-9127190","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}