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Spectral analysis using a multi-bin method for photovoltaic sites

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dc.contributor.author Maweza, Elijah L
dc.contributor.author Pratt, Lawrence E
dc.contributor.author Roro, Kittessa T
dc.date.accessioned 2022-03-23T10:42:14Z
dc.date.available 2022-03-23T10:42:14Z
dc.date.issued 2021-11
dc.identifier.citation Maweza, E.L., Pratt, L.E. & Roro, K.T. 2021. Spectral analysis using a multi-bin method for photovoltaic sites. http://hdl.handle.net/10204/12347 . en_ZA
dc.identifier.isbn 978-0-7972-1878-9
dc.identifier.uri http://hdl.handle.net/10204/12347
dc.description.abstract The high demand for low carbon energy and rapidly decreasing solar panel prices have led to a surge in solar photovoltaic (PV) plants across the globe, more than at any other time since the industry began. Combined with geographic variations in environmental conditions, this has led to a vast increase in the need for characterisation of local spectral data. However, obtaining site-specific spectral data can be difficult for average users due to their high cost, varying accuracy, and difficulty to operate and maintain. Currently, users work with spectral data obtained from the American Society for Testing and Materials (ASTM) standard, satellites, and the Simple Model for Atmospheric Transmission of Sunshine (SMARTS) Model. To describe spectra generated by any of the above-mentioned sources, researchers have used average photon energy (APE) and other parameters. In this paper, we develop a method for describing PV site-specific clear sky 12-noon spectral data based on environmental conditions and spectral properties. The approach bins measured spectra into 100 and 200 nm wavelength ranges and characterises their changes throughout the year with the plane of array (PoA) irradiance, APE, air mass (AM) and sun elevation at the site to establish relationships. The method developed showed strong correlations between the APE and the PoA irradiance, the AM and the sun elevation in three clear sky days, each month over the period of three years. The observed strong correlations between these parameters indicate that when spectra cannot be measured, the measurable site’s environmental conditions can help determine the spectral content with strong confidence. en_US
dc.format Fulltext en_US
dc.language.iso en_US en_US
dc.relation.uri https://sasec.org.za/documents/SASEC_2021_Conference_Proceedings.pdf en_US
dc.source Proceedings of the Southern African Sustainable Energy Conference (SASEC), Lanzerac, Western Cape, 17-19 November 2021 en_US
dc.subject Air mass en_US
dc.subject Average photon energy en_US
dc.subject Binning en_US
dc.subject Plane of array irradiance en_US
dc.subject Spectral distribution en_US
dc.subject Sun elevation en_US
dc.subject Low carbon energy en_US
dc.title Spectral analysis using a multi-bin method for photovoltaic sites en_US
dc.type Conference Presentation en_US
dc.description.pages 229-235 en_US
dc.description.note Paper presented during the Southern African Sustainable Energy Conference (SASEC), Lanzerac, Western Cape, 17-19 November 2021 en_US
dc.description.cluster Smart Places en_US
dc.description.impactarea Energy Supply and Demand en_US
dc.identifier.apacitation Maweza, E. L., Pratt, L. E., & Roro, K. T. (2021). Spectral analysis using a multi-bin method for photovoltaic sites. http://hdl.handle.net/10204/12347 en_ZA
dc.identifier.chicagocitation Maweza, Elijah L, Lawrence E Pratt, and Kittessa T Roro. "Spectral analysis using a multi-bin method for photovoltaic sites." <i>Proceedings of the Southern African Sustainable Energy Conference (SASEC), Lanzerac, Western Cape, 17-19 November 2021</i> (2021): http://hdl.handle.net/10204/12347 en_ZA
dc.identifier.vancouvercitation Maweza EL, Pratt LE, Roro KT, Spectral analysis using a multi-bin method for photovoltaic sites; 2021. http://hdl.handle.net/10204/12347 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Maweza, Elijah L AU - Pratt, Lawrence E AU - Roro, Kittessa T AB - The high demand for low carbon energy and rapidly decreasing solar panel prices have led to a surge in solar photovoltaic (PV) plants across the globe, more than at any other time since the industry began. Combined with geographic variations in environmental conditions, this has led to a vast increase in the need for characterisation of local spectral data. However, obtaining site-specific spectral data can be difficult for average users due to their high cost, varying accuracy, and difficulty to operate and maintain. Currently, users work with spectral data obtained from the American Society for Testing and Materials (ASTM) standard, satellites, and the Simple Model for Atmospheric Transmission of Sunshine (SMARTS) Model. To describe spectra generated by any of the above-mentioned sources, researchers have used average photon energy (APE) and other parameters. In this paper, we develop a method for describing PV site-specific clear sky 12-noon spectral data based on environmental conditions and spectral properties. The approach bins measured spectra into 100 and 200 nm wavelength ranges and characterises their changes throughout the year with the plane of array (PoA) irradiance, APE, air mass (AM) and sun elevation at the site to establish relationships. The method developed showed strong correlations between the APE and the PoA irradiance, the AM and the sun elevation in three clear sky days, each month over the period of three years. The observed strong correlations between these parameters indicate that when spectra cannot be measured, the measurable site’s environmental conditions can help determine the spectral content with strong confidence. DA - 2021-11 DB - ResearchSpace DP - CSIR J1 - Proceedings of the Southern African Sustainable Energy Conference (SASEC), Lanzerac, Western Cape, 17-19 November 2021 KW - Air mass KW - Average photon energy KW - Binning KW - Plane of array irradiance KW - Spectral distribution KW - Sun elevation KW - Low carbon energy LK - https://researchspace.csir.co.za PY - 2021 SM - 978-0-7972-1878-9 T1 - Spectral analysis using a multi-bin method for photovoltaic sites TI - Spectral analysis using a multi-bin method for photovoltaic sites UR - http://hdl.handle.net/10204/12347 ER - en_ZA
dc.identifier.worklist 25450 en_US


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