<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2733</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Hindcasting Solar Irradiance by Machine Learning using Photovoltaic Data</title>
    <abstract language="eng">This work introduces an innovative approach to calculate high-accuracy solar irradiance data for effective asset management of photovoltaic plants using Machine Learning. Ground-based pyranometers are expensive and seldom maintained, while weather service providers face limitations in spatial and temporal accuracy. A novel irradiance data model is introduced, that combines satellite weather information with data from PV plants to reconstruct historical irradiance levels with high accuracy. Our method uses existing PV arrays as "virtual sensors" to capture the local operating conditions, specifically the local irradiance incident on the array. The model was developed and validated using data from 43 medium to large-scale PV plants and two high-precision irradiance sensors. Results show superior performance compared to satellite weather data. With a root mean square deviation of 71 W/m² for global horizontal irradiation and 133 W/m² for direct normal irradiation with 5-minute resolution data, the model is about three times as accurate as the satellite weather prediction. This approach offers significant advantages in spatial resolution, reliability, and cost-effectiveness over conventional irradiance data by satellites or sensors. Utilizing SMARTBLUE AG'S dense network of thousands of monitored PV plants, the proposed methodology will enable the accurate prediction of irradiance in Germany, significantly enhancing asset management capabilities for PV plants.</abstract>
    <parentTitle language="deu">Proceedings of the 41st EU PVSEC</parentTitle>
    <enrichment key="PeerReviewed">Ja</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="RS_ProjectTitle">KICK-PV</enrichment>
    <enrichment key="RS_FundingAgency">Bayerische Forschungsstiftung</enrichment>
    <enrichment key="RS_GrantNumber">AZ-1564-22</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Maximilian Schönau</author>
    <author>Darwin Daume</author>
    <author>Markus Panhuysen</author>
    <author>T. Kreller</author>
    <author>J. Jachmann</author>
    <author>Achim Schulze</author>
    <author>Bernd Hüttl</author>
    <author>Dieter Landes</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Solar Irradiance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Photovoltaic</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hindcasting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
</export-example>
