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A Scalable, Data-driven Approach for Power Estimation of Photovoltaic Devices under Indoor Conditions
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-8382-0359
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0001-9572-3639
2019 (English)In: ENSsys'19 Proceedings of the 7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems, New York, USA: ACM Digital Library, 2019, p. 29-34Conference paper, Published paper (Refereed)
Abstract [en]

For the output power estimation of photovoltaic devices in indoor applications, models are needed that perform accurately at the low illumination levels encountered. As a robust and scalable solution, we propose a data-driven modeling method, spanning an interpolated surface between two reference I-V curves. The proposed approach is evaluated based on experimental data of two exemplar PV panels at indoor illumination levels. The results are compared to two common parameter extraction methods for the one-diode circuit model. This investigation demonstrates that the proposed surface model has a high performance under all test conditions, whereas the reference models show a performance dependency on the PV panel type. It can be concluded that the surface model is a competitive alternative for output power estimations at indoor illumination levels, removing many of the uncertainties of traditionally used physical parameter extraction and scaling methods.

Place, publisher, year, edition, pages
New York, USA: ACM Digital Library, 2019. p. 29-34
Keywords [en]
photovoltaic panel, i-v characteristics, pv model, parameter scaling, indoor energy harvesting
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-37938DOI: 10.1145/3362053.3363494Scopus ID: 2-s2.0-85076604290ISBN: 978-1-4503-7010-3 (electronic)OAI: oai:DiVA.org:miun-37938DiVA, id: diva2:1376695
Conference
7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems, New York, United States, November 2019
Funder
Knowledge Foundation, ASIS 20140323Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2020-01-29Bibliographically approved

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Ma, XinyuBader, SebastianOelmann, Bengt

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • en-US
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  • asciidoc
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