Hybrid Deep Learning and Ensemble Approach for HVAC Energy Forecasting: A GRU + Random Forest Framework

Hybrid Deep Learning and Ensemble Approach for HVAC Energy Forecasting: A GRU + Random Forest Framework | 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 Hybrid Deep Learning and Ensemble Approach for HVAC Energy Forecasting: A GRU + Random Forest Framework GANESH MURADE, Ankitkumar Sharma, Bhanu pratap Soni, GANESH Shirsat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8500219/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems are among the most energy-intensive components of modern buildings, responsible for nearly 40% of global building energy consumption. An accurate prognostication of HVAC energy consumption is consequently imperative for formulating strategies aimed at enhancing efficiency and minimizing expenses. Conventional machine learning (ML) frameworks, such as Random Forest (RF), demonstrate commendable performance yet encounter difficulties in identifying sequential patterns within time-series data. On the other hand, deep learning (DL) designs, such the Gated Recurrent Unit (GRU), are good at capturing temporal dependencies, but they often need a lot of computing power and are prone to overfitting. This paper presents a new hybrid forecasting model that combines the Gated Recurrent Unit (GRU) and Random Forest (RF) methods to take advantage of the best features of both. A two-stage process was used with the Oak Ridge National Laboratory (ORNL) FRP-2 multizone building dataset. In Stage 1, GRU models use airflow (AF) and relative humidity (RH) features to guess what the temperature will be inside (T). In Stage 2, RF models employ these anticipated temperatures, AF, and RH characteristics to figure out how much energy the HVAC system uses overall (WHRTUTotal). The results indicate that the integrated GRU + RF framework surpasses individual models, attaining enhanced predictive precision (R² = 0.886, RMSE = 755.06) while concurrently yielding significant energy conservation (13.82% utilizing predicted T and 3.41% employing actual T). These outcomes underscore the dual advantages of precision and energy efficiency, positing that hybrid models may furnish more dependable instruments for the management of energy in intelligent buildings. The recommended methodology offers a scalable basis for incorporation into real-time control strategies like Model Predictive Control (MPC). HVAC forecasting hybrid models GRU Random Forest building energy deep learning machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor invited by journal 25 Jan, 2026 Editor assigned by journal 23 Jan, 2026 Submission checks completed at journal 23 Jan, 2026 First submitted to journal 23 Jan, 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. 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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-8500219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591369029,"identity":"bb70253c-9e0a-4a94-9193-e79688559489","order_by":0,"name":"GANESH MURADE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYLACxgYILcFQYSMHYhx4QLyWM2nGYC0JRGthbDuUCGbj0yLvfvbgw587GKINbh9+eOMD24H0+WGHHwJtsZPTbcCuxfBMXrIx7xmG3A3n0owtZ/Dcyd14O80AqCXZ2OwADi0NOWbSjG1ALWcYzKR5JJ7lbpydANJyIHEbLi39b8x//gRrYf8m/cfgcLrh7PQPeLXIS+SYMfCCtfCYSTMkHE6Ql87Bb4uBxBtjad42idyZZ3iKLXsOpBlukM4pOJBggNsv8v05hh9/ttnk9p1h33jj5z8befnZ6Zs/fKiwk8OlxQAiLoEuYoBdOdiWBsIio2AUjIJRMNIBAB6iZw6c1dp3AAAAAElFTkSuQmCC","orcid":"","institution":"University of Engineering \u0026 Management","correspondingAuthor":true,"prefix":"","firstName":"GANESH","middleName":"","lastName":"MURADE","suffix":""},{"id":591369031,"identity":"20ec6c08-7138-4389-ab7e-7858a9850184","order_by":1,"name":"Ankitkumar Sharma","email":"","orcid":"","institution":"University of Engineering \u0026 Management","correspondingAuthor":false,"prefix":"","firstName":"Ankitkumar","middleName":"","lastName":"Sharma","suffix":""},{"id":591369032,"identity":"a5a499c7-cdb5-48ea-a8ce-4aad34c0e202","order_by":2,"name":"Bhanu pratap Soni","email":"","orcid":"","institution":"Fiji National University","correspondingAuthor":false,"prefix":"","firstName":"Bhanu","middleName":"pratap","lastName":"Soni","suffix":""},{"id":591369033,"identity":"92f1995c-f704-411e-a20e-4064e1f29175","order_by":3,"name":"GANESH Shirsat","email":"","orcid":"","institution":"Dr. Vithalrao Vikhe Patil College of Engineering, Ahilyanagar","correspondingAuthor":false,"prefix":"","firstName":"GANESH","middleName":"","lastName":"Shirsat","suffix":""}],"badges":[],"createdAt":"2026-01-02 10:53:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8500219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8500219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963974,"identity":"35654ada-bb95-4b11-a84c-6480497077c8","added_by":"auto","created_at":"2026-02-19 04:21:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1714049,"visible":true,"origin":"","legend":"","description":"","filename":"GBMpaper3333updatedIOT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8500219/v1_covered_189e2556-bfa7-492d-8421-62ffbae99601.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Deep Learning and Ensemble Approach for HVAC Energy Forecasting: A GRU + Random Forest Framework","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":"discover-internet-of-things","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diot","sideBox":"Learn more about [Discover Internet of Things](https://www.springer.com/journal/43926)","snPcode":"","submissionUrl":"","title":"Discover Internet of Things","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HVAC forecasting, hybrid models, GRU, Random Forest, building energy, deep learning, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8500219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8500219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeating, Ventilation, and Air Conditioning (HVAC) systems are among the most energy-intensive components of modern buildings, responsible for nearly 40% of global building energy consumption. An accurate prognostication of HVAC energy consumption is consequently imperative for formulating strategies aimed at enhancing efficiency and minimizing expenses. Conventional machine learning (ML) frameworks, such as Random Forest (RF), demonstrate commendable performance yet encounter difficulties in identifying sequential patterns within time-series data. On the other hand, deep learning (DL) designs, such the Gated Recurrent Unit (GRU), are good at capturing temporal dependencies, but they often need a lot of computing power and are prone to overfitting.\u003c/p\u003e \u003cp\u003eThis paper presents a new hybrid forecasting model that combines the Gated Recurrent Unit (GRU) and Random Forest (RF) methods to take advantage of the best features of both. A two-stage process was used with the Oak Ridge National Laboratory (ORNL) FRP-2 multizone building dataset. 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The recommended methodology offers a scalable basis for incorporation into real-time control strategies like Model Predictive Control (MPC).\u003c/p\u003e","manuscriptTitle":"Hybrid Deep Learning and Ensemble Approach for HVAC Energy Forecasting: A GRU + Random Forest Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 16:42:35","doi":"10.21203/rs.3.rs-8500219/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T03:24:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T22:05:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T12:55:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T09:28:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1616317539724834867946608778090855374","date":"2026-02-26T12:10:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210580042889725654191658597797393586931","date":"2026-02-26T10:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37727230846849843468210405438750234893","date":"2026-02-26T10:08:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T21:48:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T10:50:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222942014300824609909836853784994353608","date":"2026-02-12T11:22:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174597274528919306622797544360945060327","date":"2026-02-12T10:13:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T09:21:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-25T07:23:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-24T04:52:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T07:37:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Internet of Things","date":"2026-01-23T07:18:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-internet-of-things","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diot","sideBox":"Learn more about [Discover Internet of Things](https://www.springer.com/journal/43926)","snPcode":"","submissionUrl":"","title":"Discover Internet of Things","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a377e8b1-a64d-4af2-bf9a-fecdb65248a2","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:14:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 16:42:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8500219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8500219","identity":"rs-8500219","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}