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Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands

Author

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  • Bala Bhavya Kausika

    (Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8A, 3584 CB Utrecht, The Netherlands)

  • Wilfried G. J. H. M. van Sark

    (Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8A, 3584 CB Utrecht, The Netherlands)

Abstract

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data.

Suggested Citation

  • Bala Bhavya Kausika & Wilfried G. J. H. M. van Sark, 2021. "Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands," Energies, MDPI, vol. 14(7), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1865-:d:525439
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    References listed on IDEAS

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    1. Bala Bhavya Kausika & Panagiotis Moraitis & Wilfried G. J. H. M. Van Sark, 2018. "Visualization of Operational Performance of Grid-Connected PV Systems in Selected European Countries," Energies, MDPI, vol. 11(6), pages 1-10, May.
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    5. Lee, Minhyun & Hong, Taehoon & Jeong, Jaewook & Jeong, Kwangbok, 2018. "Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level," Energy, Elsevier, vol. 160(C), pages 213-224.
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    Cited by:

    1. Marcus Vinícius Coelho Vieira da Costa & Osmar Luiz Ferreira de Carvalho & Alex Gois Orlandi & Issao Hirata & Anesmar Olino de Albuquerque & Felipe Vilarinho e Silva & Renato Fontes Guimarães & Robert, 2021. "Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation," Energies, MDPI, vol. 14(10), pages 1-15, May.
    2. Benedetto Nastasi & Meysam Majidi Nezhad, 2021. "GIS and Remote Sensing for Renewable Energy Assessment and Maps," Energies, MDPI, vol. 15(1), pages 1-3, December.
    3. Shaban R. S. Aldhshan & Khairul Nizam Abdul Maulud & Wan Shafrina Wan Mohd Jaafar & Othman A. Karim & Biswajeet Pradhan, 2021. "Energy Consumption and Spatial Assessment of Renewable Energy Penetration and Building Energy Efficiency in Malaysia: A Review," Sustainability, MDPI, vol. 13(16), pages 1-26, August.
    4. Fausto André Valenzuela-Domínguez & Luis Alfonso Santa Cruz & Enrique A. Enríquez-Velásquez & Luis C. Félix-Herrán & Victor H. Benitez & Jorge de-J. Lozoya-Santos & Ricardo A. Ramírez-Mendoza, 2021. "Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico," Energies, MDPI, vol. 14(19), pages 1-37, October.
    5. Zhao, Shuting & Wu, Lifeng & Xiang, Youzhen & Dong, Jianhua & Li, Zhen & Liu, Xiaoqiang & Tang, Zijun & Wang, Han & Wang, Xin & An, Jiaqi & Zhang, Fucang & Li, Zhijun, 2022. "Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 1049-1064.
    6. Slawomir Gulkowski, 2023. "Modeling and Experimental Studies of the Photovoltaic System Performance in Climate Conditions of Poland," Energies, MDPI, vol. 16(20), pages 1-16, October.

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