Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods

Abstract

The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature (Tmax), minimum temperature (Tmin), sunshine hours (Hs), wind speed (Ws), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash–Sutcliffe efficiency, and determination coefficient (R2), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation; improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising Tmax, Tmin, Hs, Ws and RH input variables were about 56–41%, 44–31%, 57–46%, 35–26%, 27–16%, and 43–28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31–21%, 23–18%, and 26–25% for ANN4, ELM4, and RBF4, respectively.