Publikation:

Analysis of Current-Voltage Curves of Solar Cells by means of Artificial Neural Networks

Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2020

Autor:innen

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
https://doi.org/10.1109/PVSC45281.2020.9300479
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung

Sammlungen

Physik: Publikationen
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

2020 47th IEEE Photovoltaic Specialists Conference (PVSC). Piscataway, NJ: IEEE, 2020, pp. 1044-1046. ISBN 978-1-72816-115-0. Available under: doi: 10.1109/PVSC45281.2020.9300479

Zusammenfassung

Typically, current-voltage curves of solar cells are analyzed by computationally intense numerical fitting, e.g., using the 2-diode-model. Within this contribution, the capability of artificial neural networks (ANNs) regarding this problem is studied. It is demonstrated that ANNs can yield similar results with by far less computational effort in less time. It is shown that the accuracy of both, numeric fitting and ANNs, is mostly limited by noise.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

solar cell analysis, j(V), artificial neural networks

Konferenz

2020 47th IEEE Photovoltaic Specialists Conference (PVSC), 15. Juni 2020 - 21. Aug. 2020, Calgary, AB, Canada
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690HERGUTH, Axel, 2020. Analysis of Current-Voltage Curves of Solar Cells by means of Artificial Neural Networks. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC). Calgary, AB, Canada, 15. Juni 2020 - 21. Aug. 2020. In: 2020 47th IEEE Photovoltaic Specialists Conference (PVSC). Piscataway, NJ: IEEE, 2020, pp. 1044-1046. ISBN 978-1-72816-115-0. Available under: doi: 10.1109/PVSC45281.2020.9300479
BibTex
@inproceedings{Herguth2020Analy-54151,
  year={2020},
  doi={10.1109/PVSC45281.2020.9300479},
  title={Analysis of Current-Voltage Curves of Solar Cells by means of Artificial Neural Networks},
  isbn={978-1-72816-115-0},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2020 47th IEEE Photovoltaic Specialists Conference (PVSC)},
  pages={1044--1046},
  author={Herguth, Axel}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/54151">
    <dcterms:title>Analysis of Current-Voltage Curves of Solar Cells by means of Artificial Neural Networks</dcterms:title>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-06-30T10:12:12Z</dcterms:available>
    <dc:contributor>Herguth, Axel</dc:contributor>
    <dc:creator>Herguth, Axel</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/54151"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-06-30T10:12:12Z</dc:date>
    <dcterms:abstract xml:lang="eng">Typically, current-voltage curves of solar cells are analyzed by computationally intense numerical fitting, e.g., using the 2-diode-model. Within this contribution, the capability of artificial neural networks (ANNs) regarding this problem is studied. It is demonstrated that ANNs can yield similar results with by far less computational effort in less time. It is shown that the accuracy of both, numeric fitting and ANNs, is mostly limited by noise.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:language>eng</dc:language>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:issued>2020</dcterms:issued>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen