Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate

Photovoltaic and CSP development are related to site specification. In addition to high levels of insolation, climate and environmental conditions have a great impact on the efficiency of these technologies. Soiling stands as a major parameter influencing the degradation of transmittance and reflect...

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Main Authors: Anas Maftah, Alae Azouzoute, Massaab El Ydrissi, Ayoub Oufadel, Mohamed Maaroufi
Format: Article
Language:English
Published: Taylor & Francis Group 2022-07-01
Series:International Journal of Sustainable Energy
Subjects:
Online Access:http://dx.doi.org/10.1080/14786451.2021.1965605
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author Anas Maftah
Alae Azouzoute
Massaab El Ydrissi
Ayoub Oufadel
Mohamed Maaroufi
author_facet Anas Maftah
Alae Azouzoute
Massaab El Ydrissi
Ayoub Oufadel
Mohamed Maaroufi
author_sort Anas Maftah
collection DOAJ
description Photovoltaic and CSP development are related to site specification. In addition to high levels of insolation, climate and environmental conditions have a great impact on the efficiency of these technologies. Soiling stands as a major parameter influencing the degradation of transmittance and reflectance for PV glass and CSP reflectors respectively, which decreases electrical performance. In this paper, the PV system and TraCS system (Tracking Cleanliness System) are used to evaluate the soiling effect in two sites with different climate conditions. Environmental variables such as air temperature, relative humidity, wind speed, wind direction, and precipitation, are used as inputs to model soiling. The relationship between different input and output variables are analysed using two linear regression models in a simple form, and with the interaction between model predictors. An in-depth analysis is performed with an artificial neural network (ANN) model, to assess its ability to resolve the complexity of the soiling problem in relation to environmental parameters, and also for comparison with the linear models. The results show that in the region with relatively low rainfall soiling is significantly high and the soiling loss reach up in the summer season –1.07%/day. However, in the region with significant rainy days, the soiling loss is in the range of –0.18%/day in the same period of the year. On the other hand, the ANN model performed significantly in estimating the soiling ratio in comparison to the linear models in terms of R2 values and statistical indicators.
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spelling doaj.art-49c896c993a34cdcaa4954c4307b21942023-09-20T10:33:47ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2022-07-0141662964510.1080/14786451.2021.19656051965605Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climateAnas Maftah0Alae Azouzoute1Massaab El Ydrissi2Ayoub Oufadel3Mohamed Maaroufi4Mohammed V UniversityInstitut de Recherche en Energie Solaire et Energies Nouvelles – IRESEN, Green Energy ParkInstitut de Recherche en Energie Solaire et Energies Nouvelles – IRESEN, Green Energy ParkUniversity Sidi Mohamed Ben AbdellahMohammed V UniversityPhotovoltaic and CSP development are related to site specification. In addition to high levels of insolation, climate and environmental conditions have a great impact on the efficiency of these technologies. Soiling stands as a major parameter influencing the degradation of transmittance and reflectance for PV glass and CSP reflectors respectively, which decreases electrical performance. In this paper, the PV system and TraCS system (Tracking Cleanliness System) are used to evaluate the soiling effect in two sites with different climate conditions. Environmental variables such as air temperature, relative humidity, wind speed, wind direction, and precipitation, are used as inputs to model soiling. The relationship between different input and output variables are analysed using two linear regression models in a simple form, and with the interaction between model predictors. An in-depth analysis is performed with an artificial neural network (ANN) model, to assess its ability to resolve the complexity of the soiling problem in relation to environmental parameters, and also for comparison with the linear models. The results show that in the region with relatively low rainfall soiling is significantly high and the soiling loss reach up in the summer season –1.07%/day. However, in the region with significant rainy days, the soiling loss is in the range of –0.18%/day in the same period of the year. On the other hand, the ANN model performed significantly in estimating the soiling ratio in comparison to the linear models in terms of R2 values and statistical indicators.http://dx.doi.org/10.1080/14786451.2021.1965605photovoltaicconcentrated solar powersoilingann modelenvironmental parameters
spellingShingle Anas Maftah
Alae Azouzoute
Massaab El Ydrissi
Ayoub Oufadel
Mohamed Maaroufi
Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate
International Journal of Sustainable Energy
photovoltaic
concentrated solar power
soiling
ann model
environmental parameters
title Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate
title_full Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate
title_fullStr Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate
title_full_unstemmed Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate
title_short Soiling investigation for PV and CSP system: experimental and ANN modelling analysis in two sites with different climate
title_sort soiling investigation for pv and csp system experimental and ann modelling analysis in two sites with different climate
topic photovoltaic
concentrated solar power
soiling
ann model
environmental parameters
url http://dx.doi.org/10.1080/14786451.2021.1965605
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