Abstract

The abundant spatial and temporal availability of solar energy have been fueling many researches and have been the reason for the proliferation of solar energy applications in the past decades. Many of these applications involve heavy investments and thus require highly accurate and reliable long-term average solar data for efficient deployment of solar energy technologies. Since ground stations are costly, site-specific, scarce and cannot provide long-term solar data, satellite derived data is the next best alternative. However, satellite models are often unable to capture the complex local climatological variations of a given site. As such, short-term high precision solar ground measurements are used to train the satellite model so as to improve the accuracy for long-term solar estimates. There exist several site adaptation techniques to perform this task. However, to the knowledge of the researchers, no comparative study has been conducted to establish which site adaptation technique is the most effective. In this study, a robust methodology has been proposed to compare the effectiveness of four site adaptation techniques for monthly and yearly datasets using novel key performance indicators. Ground measurements from twelve stations in the tropical islands of Mauritius, Rodrigues and Agalega were used to adapt satellite data obtained from HelioClim-3 database using different techniques. Three new non-linear site adaptation techniques have been proposed: Adjustment technique (Technique 2), Compensation technique (Technique 3) and Relationship technique (Technique 4). The first part of the study showed that 67% to 100% of the datasets were best approximated with sixth order polynomials for the three non-linear techniques. The second part revealed that Technique 1 (linear method) and Technique 2 were most appropriate for maximum and average datasets respectively. The results were such that Technique 2 and Technique 1 provided best approximations for 77.9% to 83.3% and 40.7% to 58.3% of average and maximum datasets respectively. In the third part of the study, only Technique 2 provided remarkable improvements for all statistical metrics with respect to the original monthly datasets (113-118 datasets). The analysis reported 57.6%-89.9%, 49.8%-68.0%, 67.4%-87.3%, 53.8%-63.1%, 45.0%-64.0%, 7.7%-9.6% and 2.7%-4.7% mean improvements for MBE, MABE, MPE, MAPE, RMSE, NSE and COD respectively for Technique 2. Similar results were observed for yearly average datasets while the appreciation was shared among all four techniques for yearly maximum datasets, with Technique 1 having a slight advantage.

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