Influence of environmental factors on the power produced by photovoltaic panels artificially weathered
Solar energy is an important renewable energy source and a great option to mitigate the greenhouse gases produced by fossil fuels in electricity production. The common way to use solar energy is through photovoltaic technology. This technology converts solar photons into electricity. However, power...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/406964 |
| Acesso em linha: | https://hdl.handle.net/2117/406964 https://dx.doi.org/10.1016/j.rser.2023.113831 |
| Access Level: | acceso abierto |
| Palavra-chave: | Photovoltaic power generation Solar energy Renewable energy Sustainability Environmental statistics Weathering Energia solar fotovoltaica Àrees temàtiques de la UPC::Energies::Recursos energètics renovables::Recursos solars fotovoltaics |
| Resumo: | Solar energy is an important renewable energy source and a great option to mitigate the greenhouse gases produced by fossil fuels in electricity production. The common way to use solar energy is through photovoltaic technology. This technology converts solar photons into electricity. However, power production by photovoltaic panels (PV) is strongly related to environmental, manufactured, and maintenance factors. The present work analyzed the exposure of PV panels to environmental conditions at different stages in their lifetime (new, five, ten, 15 years, and 20 years). For this purpose, the PV panel was subjected to a simulated weathering process. A Generalized Linear Model (GLM) approach was used to evaluate the influence of environmental conditions (meteorological and air pollution) on power production, which follows a Binomial Negative distribution due to the overdispersion. The proposed method was performed in the tropical Andean during the winter season and in the presence of the El Niño Southern Oscillation (ENSO). Generally, the specific humidity and temperature were the meteorological significant covariates, while the PM2.5 was the air pollution significant covariate. Higher power production efficiencies were obtained in the presence of precipitation and wind velocity as significant covariates. The model evaluation had adequate criteria values, NSE between 0.76 and 0.96, and correlation coefficient between 0.87 and 0.98 using a new and weathered 15-year PV panel, respectively. |
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