Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches

Although the sunshine-based models generally have a better performance than temperature-based models for estimating solar radiation, the limited availability of sunshine duration records makes the development of temperature-based methods inevitable. This paper presents a comparative study between Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Wavelet Regression (WR) and 5 selected temperature-based empirical models for estimating the daily global solar radiation. A new combination of inputs including four readily accessible parameters have been employed: daily mean clearness index (K-T), temperature range (Delta T), theoretical sunshine duration (N) and extraterrestrial radiation (R-a). Ten statistical indicators in a form of GPI (Global Performance Indicator) is used to ascertain the suitability of the models. The performance of selected models across the range of solar radiation values, was depicted by the quantile-quantile (Q-Q) plots. Comparing these plots makes it evident that ANNs can cover a broader range of solar radiation values. The results shown indicate that the performance of ANN model was clearly superior to the other models. The findings also demonstrated that WR model performed well and presented high-accuracy in estimations of daily global solar radiation.