Benchmarking on improvement and site-adaptation techniques for modeled solar radiation datasets. (1st May 2020)

• Record Type: Journal Article
• Authors: Polo, Jesus; Fernández-Peruchena, Carlos; Salamalikis, Vasileios; Mazorra-Aguiar, Luis; Turpin, Mathieu; Martín-Pomares, Luis; Kazantzidis, Andreas; Blanc, Philippe; Remund, Jan
• Is Part Of: Solar energy. Volume 201(2020)
• Page Start: 469
• Page End: 479
• Publication Date: 2020-05-01
• Subjects: Satellite-derived solar radiation -- Site adaptation -- Bankability of data for solar projects -- Modeled solar radiation data; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2020.03.040
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Site adaptation techniques are used for improving the quality of modeled solar data. Task 16 IEA-PVPS has promoted effort in this topic. Benchmarking of different techniques result in recommendation for site adaptation. Significant improvement of modeled solar radiation can be potentially achieved. Abstract: High-accuracy solar radiation data are needed in almost every solar energy project for bankability. Time series of solar irradiance components that spans decades can be supplied by satellite-derived irradiance or by reanalysis models, with very various types of uncertainty associated to the specific approaches taken and quality of boundary conditions information. In order to improve the reliability of these modeled datasets, comparison with ground measurements over a short period of time can be used for correcting some aspects, bias mainly, of the modeled data by using different methodologies; this procedure is known as site adaptation. Therefore, a benchmarking exercise that uses different site adaptation techniques was proposed within the Task 16 IEA-PVPS activities. In this work, over ten different site-adaptation techniques have been used for assessing the accuracy improvement, using ten different datasets covering both satellite-derived and reanalysis solar radiation data. The effectiveness of these methods is found not universal or spatially homogeneous, but in general, it can be stated that significant improvements can be achieved eventually in most sites and datasets.