Abstract:
Knowledge of the amount of solar radiation available in a location is important for solar energy systems, architectural designs, agronomy, and installation of pyranometers. Some developing countries do not have good quality meteorological stations that can directly measure global solar radiation. Thus, several empirical methods were developed to estimate global solar radiation. This study uses two temperature-based models which are Hargreaves - Samani and Clemence models. Four selected stations from the Southern African Universities Radiometric Network (SAURAN) for this study are University of KwaZulu–Natal, Howard college (KZH), University of Stellenbosch (SUN), Nelson Mandela University (NMU) and University of Venda (UNV). A three-year (2014-2016) temperature data for each station were sourced from SAURAN. The performance of the two models was validated using statistical analysis that is, Mean Percentage Error (MPE), Mean Bias Error (MBE), Root Mean Square (RMSE), Coefficient of Determination (R2) and t-statistical value (t). Both models obtained acceptable values of MBE, MPE, RMSE, R2 and t in KZH, NMU and UNV stations. Both models achieved the best values of MBE from 2014 to 2016, ranging from -0.0099 to 0.0147 in KZH station, followed by NMU with MBE values ranging from - 0.0293 to -0.0014, -0.0104 to 0.0330 for SUN station, 0.0241 to 0.0245 for UNV station. The models achieved MPE values between ± 10 % in all the stations. The R2 values for both models are close to 1, while the t-statistic values of one, which is less than critical value, was achieved by the models from all selected stations. This suggests that both models have got capacity to estimate global solar radiation in all the selected areas of study. However, the higher values of MBE and RMSE also revealed high level of overestimation by the models in SUN station. Therefore, this study has found evidence that both Hargreaves - Samani & Clemence models can be best recommended for estimating global solar radiation in KZH, NMU and UNV stations and areas with similar climatic and meteorological conditions.
