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Open AccessArticle

Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

1
National Renewable Energy Centre (CENER), C/Isaac Newton n 4, 41092 Sevilla, Spain
2
Photovoltaic Solar Energy Unit (Energy Department, CIEMAT), Avda. Complutense 40, 28040 Madrid, Spain
3
Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha 34110, Qatar
4
University Institute for Intelligent Systems and Numerical Applications in Engineering, University of Las Palmas de Gran Canaria, Edificio Central del Parque Tecnológico, 35017 Las Palmas de Gran Canaria, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2127; https://doi.org/10.3390/rs12132127
Received: 1 June 2020 / Revised: 24 June 2020 / Accepted: 30 June 2020 / Published: 2 July 2020
(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)
The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the adequate solar plant design and reducing the uncertainty of its yield estimates. This work presents a procedure for improving the accuracy of modeled solar irradiance series through site-adaptation with coincident ground-based measurements relying on the use of a regression preprocessing followed by an empirical quantile mapping (eQM) correction. It was tested at nine sites in a wide range of latitudes and climates, resulting in significant improvements of statistical indicators of dispersion, distribution similarity and overall performance: relative bias is reduced on average from −1.8% and −2.3% to 0.1% and 0.3% for GHI and DNI, respectively; relative root mean square deviation is reduced on average from 17.9% and 34.9% to 14.6% and 29.8% for GHI and DNI, respectively; the distribution similarity is also improved after the site-adaptation (KSI is 3.5 and 3.9 times lower for GHI and DNI at hourly scale, respectively). The methodology is freely available as supplementary material and downloadable as R-package from SiteAdapt. View Full-Text
Keywords: site-adaptation; data fusion; bankability of solar projects; satellite-derived irradiance; bias removal site-adaptation; data fusion; bankability of solar projects; satellite-derived irradiance; bias removal
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MDPI and ACS Style

Fernández-Peruchena, C.M.; Polo, J.; Martín, L.; Mazorra, L. Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure. Remote Sens. 2020, 12, 2127.

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