Accurate and Scalable Receiver-Level Flux Prediction: A Fully Data-Driven Solution

Accurate and Scalable Receiver-Level Flux Prediction: A Fully Data-Driven Solution | 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 Accurate and Scalable Receiver-Level Flux Prediction: A Fully Data-Driven Solution Mathias Kuhl, Max Pargmann, Daniel Maldonado Quinto, Robert Pitz-Paal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6192986/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 Concentrated Solar Technologies (CST) systems, particularly central tower configurations with heliostat fields, play a critical role in the renewable energy landscape. By focusing sunlight from thousands of heliostats onto a central receiver, these systems generate high-temperature heat, which serves as a key resource for dispatchable power generation and industrial processes. Accurate receiver-level flux prediction, which depends on precise heliostat characterization, is essential for optimizing efficiency and operational control. However, existing characterization methods face trade-offs between accuracy and scalability, limiting their practicality for large-scale deployment. To overcome these limitations, this study introduces a fully data-driven framework that unifies heliostat characterization and flux prediction, leveraging operational data from standard calibration procedures. Expanding upon previous work that employed StyleGAN for beam-characterization-based predictions, this approach advances the methodology to achieve accurate receiver-level flux predictions. While the prior method demonstrated a proof of concept for a unified data-driven approach, it remained constrained to flux predictions on the calibration target itself. This study introduces key innovations, including aim point generalization strategies and a novel receiver projection technique, effectively bridging the gap between beam-characterization-based heliostat characterization and accurate receiver-level flux predictions. The proposed Transformer-based architecture achieves receiver-level focal spot prediction errors below 12%, exceeding the accuracy of state-of-the-art deflectometry-enhanced ray tracing. By relying exclusively on standard calibration images, the method remains both cost-efficient and scalable, offering a practical solution for large-scale CST applications. Energy Engineering Solar power tower Flux Density Prediction Camera-Target Method Heliostat Machine Learning Full Text Additional Declarations The authors declare no competing interests. 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-6192986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426494357,"identity":"b7653c3a-f39d-4f32-85ac-80afd3639566","order_by":0,"name":"Mathias Kuhl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3OMQuCQBTA8SfBuRy0Pgn8BsG1SEIf5sRVc3MKabJFmv04Fwe1RK6NguDU4BRO0VHSeDo23H843nA/3gMwmf4zAly9CNQSHVmrkY+J2Y/AqSQ4kcBAJJ1ClofqXNe7DJwyFnKTYgJ2KLTEu4Y242cJC9xyGd3Q39NWv8YTIUFOBLgYMRnnyEANelI1iryyL/E/JOn05K62BPlMHaaI9d2iFYo0HgZHSZ3iwU7FDRmh7dhhQev0z8zFS7Sq+3TD5nZY69cM0d9EJv03mUwmk7Y3Gl5AsmbUyhgAAAAASUVORK5CYII=","orcid":"","institution":"DLR, Institute of Solar Research","correspondingAuthor":true,"prefix":"","firstName":"Mathias","middleName":"","lastName":"Kuhl","suffix":""},{"id":426494358,"identity":"dad55837-8d9b-40f4-904f-a3b61050cb6e","order_by":1,"name":"Max Pargmann","email":"","orcid":"","institution":"DLR, Institute of Solar Research","correspondingAuthor":false,"prefix":"","firstName":"Max","middleName":"","lastName":"Pargmann","suffix":""},{"id":426494359,"identity":"87beb5a1-fae0-4d3b-a068-cd5723b1e10f","order_by":2,"name":"Daniel Maldonado Quinto","email":"","orcid":"","institution":"DLR, Institute of Solar Research","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Maldonado","lastName":"Quinto","suffix":""},{"id":426494360,"identity":"c0415922-c727-4cde-9f16-946bb2740d2a","order_by":3,"name":"Robert Pitz-Paal","email":"","orcid":"","institution":"DLR, Institute of Solar Research","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Pitz-Paal","suffix":""}],"badges":[],"createdAt":"2025-03-10 07:42:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6192986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6192986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78320468,"identity":"8ea88edd-64d1-4980-b52b-665fe3e02486","added_by":"auto","created_at":"2025-03-12 05:01:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4307621,"visible":true,"origin":"","legend":"","description":"","filename":"TransformerSolarEnergy3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6192986/v1_covered_fa192aa1-8b7d-46d1-b0ce-b0915c5e37cf.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAccurate and Scalable Receiver-Level Flux Prediction: A Fully Data-Driven Solution\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[{"identity":"c907cedd-0498-4f77-acee-0f27b5543790","identifier":"10.13039/501100001656","name":"Helmholtz-Gemeinschaft","awardNumber":"ZT-I-PF-5-069","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"German Aerospace Center","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":"Solar power tower, Flux Density Prediction, Camera-Target Method, Heliostat, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-6192986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6192986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConcentrated Solar Technologies (CST) systems, particularly central tower configurations with heliostat fields, play a critical role in the renewable energy landscape. By focusing sunlight from thousands of heliostats onto a central receiver, these systems generate high-temperature heat, which serves as a key resource for dispatchable power generation and industrial processes. Accurate receiver-level flux prediction, which depends on precise heliostat characterization, is essential for optimizing efficiency and operational control. However, existing characterization methods face trade-offs between accuracy and scalability, limiting their practicality for large-scale deployment.\u003c/p\u003e\n\u003cp\u003eTo overcome these limitations, this study introduces a fully data-driven framework that unifies heliostat characterization and flux prediction, leveraging operational data from standard calibration procedures. Expanding upon previous work that employed StyleGAN for beam-characterization-based predictions, this approach advances the methodology to achieve accurate receiver-level flux predictions. 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