Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation

As a primary input in meteorology, the accuracy of solar radiation simulations affects hydrological, climatological, and agricultural studies and sustainable development practices and plans. With the advent of machine learning models and their proven capabilities in modelling the hydro-meteorological phenomena, it is necessary to find the best model suitable for each phenomenon. Models performance depends upon their structure and the input data set. Therefore, some well-known and newest machine learning models with different inputs are tested here for solar radiation simulation in Illinois, USA. The data mining models of Support Vector Machine (SVM), Gene Expression Programming (GEP), Long Short-Term Memory (LSTM), and their combination with the wavelet transformation building a total of six model structures are applied to five data sets to examine their suitability for solar radiation simulation. The five input data sets (SCN_1 to SCN_5) are based on five readily accessible parameters, namely extraterrestrial radiation (R a ), maximum and minimum air temperature (T min , T max ), corrected clear-sky solar irradiation (ICSKY), and Day of Year (DOY). The LSTM outperformed other models, consulting the performance measures of RMSE, SI, MAE, SS RMSE , and SS MAE . Of the different input data sets, in general, SCN_4 was the best input scenario for predicting global daily solar radiation using Ra, Tmax, Tmin, and DOY variables. Overall, six machine learning based models showed acceptable performances for estimating solar radiation, with the LSTM machine learning technique being the most recommended.