From Simulation to Operation: AI-Based Environmental Control Systems Bridging the Performance Gap in Sustainable University Buildings – Case Study of Damietta

From Simulation to Operation: AI-Based Environmental Control Systems Bridging the Performance Gap in Sustainable University Buildings – Case Study of Damietta | 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 From Simulation to Operation: AI-Based Environmental Control Systems Bridging the Performance Gap in Sustainable University Buildings – Case Study of Damietta Huda Albaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9370182/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 As The energy performance gap (EPG), defined as the discrepancy between predicted and actual building energy consumption, remains a persistent challenge in sustainable building design. This study investigates the implementation of AI-based environmental control systems to bridge the performance gap in university buildings located in Damietta, Egypt. A hybrid framework integrating calibrated Energy Plus simulation, deep learning-based load forecasting (CNN–LSTM), and reinforcement learning HVAC optimization was developed and validated using 12 months of operational data. Results indicate that AI-driven predictive control reduced the performance gap by 22–28%, improved indoor environmental quality compliance from 69% to 93%, and reduced operational costs by approximately 19%. The findings demonstrate that transitioning from static simulation-based design to adaptive AI-driven operational control significantly enhances energy reliability and sustainability outcomes in hot-humid climates. Energy Performance Gap Smart Buildings Artificial Intelligence Deep Learning Reinforcement Learning HVAC Optimization University Buildings Sustainable Buildings 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-9370182","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621830753,"identity":"84476f95-a8ba-4603-af5b-0d828c04d764","order_by":0,"name":"Huda Albaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACAwbmNiBlkwDlMxOjhRGkJY10LYdJ0GLOfrDtwY8/5/P4pc8Yf2CosE5sYG+/gFeLZU9iu2EPz+1iyb4cMwmGM+mJDTxnCvA77EBimwSPxO3EDWd4zICOPJzYIJGTgF/L+Ydtkn8MzoG0GH9g/AfUIv+GgJYbiW3SPAkHQFoMJBgbQLawHyCg5WG7scyB5MSZPWxlEgnH0o3beHLw6gA6LPnYwzd/7BL7eZg3f/hQYy3bz378AX49KADkCTYGHgMStEAAOym2jIJRMApGwQgAACd+Sge38J61AAAAAElFTkSuQmCC","orcid":"","institution":"Horus University Egypt","correspondingAuthor":true,"prefix":"","firstName":"Huda","middleName":"","lastName":"Albaz","suffix":""}],"badges":[],"createdAt":"2026-04-09 14:56:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9370182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9370182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107480393,"identity":"ca2151da-e1cd-4ebf-92af-8d277bd34517","added_by":"auto","created_at":"2026-04-22 02:09:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":591985,"visible":true,"origin":"","legend":"","description":"","filename":"springernaturelatextemplateDamita204withoutname.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9370182/v1_covered_65a8c4ac-7270-4234-adcf-8e47b11e0f6f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFrom Simulation to Operation: AI-Based Environmental Control Systems Bridging the Performance Gap in\u003cbr\u003e\nSustainable University Buildings – Case Study of Damietta\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":"Energy Performance Gap, Smart Buildings, Artificial Intelligence, Deep Learning, Reinforcement Learning, HVAC Optimization, University Buildings, Sustainable Buildings","lastPublishedDoi":"10.21203/rs.3.rs-9370182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9370182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs The energy performance gap (EPG), defined as the discrepancy between predicted and actual building energy consumption, remains a persistent challenge in sustainable building design. 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