Water Management in Data Centers Using Ensemble Learning Models

Water Management in Data Centers Using Ensemble Learning Models | 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 Water Management in Data Centers Using Ensemble Learning Models Mario Pinto, Tanmay Kadam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6416747/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 Data centers are using enormous volumes of water for maintenance and cooling as a result of the growing need for computational power. Reducing operating expenses and the environmental impact requires effective water management. In order to minimize water usage in data centers, this study proposes a predictive analytics framework that makes use of ensemble learning techniques, specifically Random Forest Regressor, XGBoost Regressor, and Gradient Boosting Regressor. To find patterns of excessive consumption, the suggested method combines anomaly detection with real-time monitoring. To improve the effectiveness of predictive models, the framework uses a multifaceted strategy that includes adaptive thresholding, outlier detection, and time-series forecasting. Significant gains in anomaly detection rates and forecast accuracy are shown by a comparative performance analysis. Additionally, proactive intervention is made possible by the integration of real-time information, which lowers water waste and guarantees operational sustainability. The experimental findings demonstrate the suggested methodology’s potential for widespread implementation in data centers and prove its resilience. Water management Data centers Ensemble learning Anomaly detection Predictive analytics Full Text 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-6416747","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453019381,"identity":"93d541b7-d101-475a-894b-7d24f64a7956","order_by":0,"name":"Mario Pinto","email":"","orcid":"","institution":"Goa Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Pinto","suffix":""},{"id":453019382,"identity":"ce0a1c2c-bb67-4c11-8779-6c0068059e79","order_by":1,"name":"Tanmay Kadam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBADAzb29oMPgAwePkJKeWBa+HjOJBuABNiI1iInkWAmAWIR1GLPfvbohp9tdsZsDAlplV9z7GTYGJgfPrqBzxaevLSbvW3JZmwMB4/dlt2WDHQYm7FxDl6H5Zjd4G1jtmFjbEi7LbmNGaiFh00arxb+N2Y3/7bV27AxM5gVS26rJ0KLRI7Zbd62w2ZsbAxmjB+3HSZCy403Zrdlzh03ZuPhSZZm3Hach42ZgF/Y+3PMbr4pqzacP//5wY8/t1Xb87M3P3yMTwsYMELjghkcS8yElIPBH6jWH0SpHgWjYBSMgpEGAPuoQaUVo9YBAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-1698-9676","institution":"Goa Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Tanmay","middleName":"","lastName":"Kadam","suffix":""}],"badges":[],"createdAt":"2025-04-10 05:53:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6416747/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6416747/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85683239,"identity":"838aa13d-cb5d-4b26-82d3-6d5f8f713023","added_by":"auto","created_at":"2025-06-30 15:33:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":498315,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6416747/v1_covered_a938283a-ee33-4bde-9739-786d8dc39a82.pdf"}],"financialInterests":"","formattedTitle":"Water Management in Data Centers Using Ensemble Learning Models","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":"Water management, Data centers, Ensemble learning, Anomaly detection, Predictive analytics","lastPublishedDoi":"10.21203/rs.3.rs-6416747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6416747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Data centers are using enormous volumes of water for maintenance and cooling as a result of the growing need for computational power. 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