Energy Forecasting In LED Video Display Panels Using Deep Learning

Energy Forecasting In LED Video Display Panels Using Deep 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 Energy Forecasting In LED Video Display Panels Using Deep Learning RAMESH R, Bazilabanu A This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4201097/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 2 You are reading this latest preprint version Abstract In recent years, energy usage in LED Video Wall Display Panels (LED-VWDPs) has increased massively; Predicting energy consumption is a challenging and crucial task for LED-VWDPs. Hence Real-time energy usage issues can be resolved by predicting future energy demand. Deep learning plays an important role in more accurate prediction in energy forecasting. In this article, two approaches are presented: the first makes use of a recurrent neural network (RNN), and the other utilizes a long short-term memory (LSTM) network.In comparison to other existing machine learning techniques, such as ARIMA and Facebook Prophet, Long Short-Term Memory (LSTM) in deep learning is better at handling time-series datasets and projecting future energy demand. It predicts the actual energy usage of LED-VWDP and forecasts the futureenergydemandofLED-VWDP. A vast dataset of LED-VWDP energy consumption is utilized in this paper. Through the proposed RNN and LSTM methods, users can identify the individual energy usage of LED-VWDP and predict its future energy demand.The results of the proposed methods are evaluated alongside those of the existing methods in order to forecast energy usage. The results are used to evaluate the performance of forecasting future energy demands, depending on the number of epochs. The accuracy of RNN and LSTM ranges from 82.02–95.86%. The predictions have been made for a period of two months, encompassing short-and mid-term forecasts.In evaluating the comparison of various machine and deep learning models, LSTM is found to be accurate with an average root mean square error of 0.5 in forecasting energy consumption. LED Video Wall Display Panel (LED-VWDPs) machine learning methods ARIMA Facebook Prophet Recurrent Neural Networks long short-term memory energy demand Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 04 Apr, 2024 First submitted to journal 01 Apr, 2024 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-4201097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287393469,"identity":"d638b84a-96ec-43c1-95c2-4ae3238fa8de","order_by":0,"name":"RAMESH R","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYLACxgYgwcx84PCPiv9yIIEDD4jTwpb4mOEMszFYSwJRWhh4jI0Z25gTwWx8WszbTyc+/LnjnrzBcQYz6cI2tvT5YYcfAm2xk9NtwK5F5kzuZmPeM8WGGw4zpEnPOMeTu/F2mgFQS7Kx2QHsWiQYcrdJM7YlMM5sZjgmwVMmkbtxdgJIy4HEbbi08L/d/vNnW4L9zGbGNgkeNoN0w9npH/BrkcjdxsDblpDYz8zMbMzTlpAgL51DwBaJt5ulgVqS+5nZGB/OOHPAcIN0TsGBBAM8fuHP3fgR6DDbNv7zHw58qDggLz87ffOHDxV2cri0YAIDsEoDYpWDgHwDKapHwSgYBaNgJAAA5adj5sIsOVYAAAAASUVORK5CYII=","orcid":"","institution":"Bannari Amman Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"RAMESH","middleName":"","lastName":"R","suffix":""},{"id":287393470,"identity":"4a587c27-89c5-49b9-a51c-9145ff172d0f","order_by":1,"name":"Bazilabanu A","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bazilabanu","middleName":"","lastName":"A","suffix":""}],"badges":[],"createdAt":"2024-04-01 13:57:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4201097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4201097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54337254,"identity":"ebce4b8d-bc7b-4e30-83a5-acccdda2a29d","added_by":"auto","created_at":"2024-04-09 03:37:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":363876,"visible":true,"origin":"","legend":"","description":"","filename":"EnergyForecastingInLEDVideoDisplayPanelUsingDeepLearning2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201097/v1_covered_76ab377f-144d-46a9-8312-5f236ee4eb5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Energy Forecasting In LED Video Display Panels Using Deep 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":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LED Video Wall Display Panel (LED-VWDPs), machine learning methods, ARIMA, Facebook Prophet, Recurrent Neural Networks, long short-term memory, energy demand","lastPublishedDoi":"10.21203/rs.3.rs-4201097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4201097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, energy usage in LED Video Wall Display Panels (LED-VWDPs) has increased massively; Predicting energy consumption is a challenging and crucial task for LED-VWDPs. Hence Real-time energy usage issues can be resolved by predicting future energy demand. Deep learning plays an important role in more accurate prediction in energy forecasting. In this article, two approaches are presented: the first makes use of a recurrent neural network (RNN), and the other utilizes a long short-term memory (LSTM) network.In comparison to other existing machine learning techniques, such as ARIMA and Facebook Prophet, Long Short-Term Memory (LSTM) in deep learning is better at handling time-series datasets and projecting future energy demand. It predicts the actual energy usage of LED-VWDP and forecasts the futureenergydemandofLED-VWDP. A vast dataset of LED-VWDP energy consumption is utilized in this paper. Through the proposed RNN and LSTM methods, users can identify the individual energy usage of LED-VWDP and predict its future energy demand.The results of the proposed methods are evaluated alongside those of the existing methods in order to forecast energy usage. The results are used to evaluate the performance of forecasting future energy demands, depending on the number of epochs. The accuracy of RNN and LSTM ranges from 82.02\u0026ndash;95.86%. The predictions have been made for a period of two months, encompassing short-and mid-term forecasts.In evaluating the comparison of various machine and deep learning models, LSTM is found to be accurate with an average root mean square error of 0.5 in forecasting energy consumption.\u003c/p\u003e","manuscriptTitle":"Energy Forecasting In LED Video Display Panels Using Deep Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 03:29:44","doi":"10.21203/rs.3.rs-4201097/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-04-04T07:32:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wireless Networks","date":"2024-04-01T13:55:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d353ae64-e7ee-46c0-9ed8-30451ef11ad0","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-09T03:29:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 03:29:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4201097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4201097","identity":"rs-4201097","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}