Indoor illumination estimation based on improved black winged kite optimized Transformer

Indoor illumination estimation based on improved black winged kite optimized Transformer | 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 Indoor illumination estimation based on improved black winged kite optimized Transformer Lirong Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9256108/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract In order to improve the accuracy and adaptability of indoor illumination estimation in intelligent lighting systems, this paper proposes a Transformer model based on the Improved Black Winged Kite Optimization Algorithm (CBKA) and constructs the CBKA Transformer architecture. The model integrates multi head self attention mechanism, time aware position encoding (TPE), and learnable spatial embedding to effectively model complex spatiotemporal dependency characteristics. The experiment was validated using multi season and multi weather illumination data collected through DIALux simulation. On the office environment test set, CBKA Transformer achieved an RMSE of 6.05 lx and an R² of 0.978, with a prediction accuracy improvement of 34.5% compared to the standard Transformer. The ablation experiment showed that each module significantly improved performance, especially CBKA optimization reduced the number of convergence iterations of the model to 35 rounds. The results indicate that the method has good generalization ability and practical value. CBKA Transformer Illuminance prediction Multi head attention Time aware encoding Intelligent Light System Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 28 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. 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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-9256108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629678335,"identity":"dda26519-5db0-41f1-a9b9-35363c7c0476","order_by":0,"name":"Lirong Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACA2YwZZcAJBgffACSfAwMzMRoSQZpYTacwcAgwUZQC4Q6CNLCJs1DlBZ23oePC34dyOOXbr8gbVNxuI6NvfmwAUONTTRuh7EbG8/sO1AsOedMgXHOmcMSbDzHgM48lpbbgFMLG5s0b8+BxA03chKSc9uAWiRyjA8wNhwmrGU/UMthS6K18Pw4mLhBIv1gMyNUSwIBLczGvA3JiTNu5DAz9pxJl2wD+sUgAY9f7PuPMT7m+WOX2D8j/fmPHxXW/PzAEJP4UGODUwsYMLaBSB4DhEgCPuVg8AdEsD8gqG4UjIJRMApGJgAAYHNUf6nPBSsAAAAASUVORK5CYII=","orcid":"","institution":"Jingzhou vocational college of technology","correspondingAuthor":true,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2026-03-29 03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9256108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9256108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007057,"identity":"17a7840f-134d-45e9-9095-86a2420777f1","added_by":"auto","created_at":"2026-04-28 12:58:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1758712,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9256108/v1_covered_34e87279-7d80-4189-a81f-8e29ccba0097.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Indoor illumination estimation based on improved black winged kite optimized Transformer","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-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CBKA Transformer, Illuminance prediction, Multi head attention, Time aware encoding, Intelligent Light System","lastPublishedDoi":"10.21203/rs.3.rs-9256108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9256108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to improve the accuracy and adaptability of indoor illumination estimation in intelligent lighting systems, this paper proposes a Transformer model based on the Improved Black Winged Kite Optimization Algorithm (CBKA) and constructs the CBKA Transformer architecture. 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