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 (T_max), minimum temperature (T_min), sunshine hours (H_s), wind speed (W_s), 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 T_max, T_min, H_s, W_s 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.

本研究调查了基于气候数据对月度太阳辐射进行建模的新的综合方法贝叶斯模型平均（BMA）的潜力。数据记录涵盖每月最高温度（T _max），最低温度（T _min），日照小时（H _s），风速（W _s）），相对湿度（RH）和从土耳其两个气象站获得的太阳辐射值。将BMA估计值与人工神经网络（ANN），极限学习机（ELM），径向基函数（RBF）以及它们与小波变换技术（小波-ANN或WANN，小波-ELM或WELM和小波-RBF或WRBF）。三种评估标准，例如均方根误差（RMSE），纳什-萨特克利夫效率和测定系数（R ²），用于测量所采用方法的准确性。结果表明，BMA4模型比六个机器学习模型在估计每月太阳辐射方面具有更高的准确性；改进了包含T _max，T的ANN4，ELM4，RBF4，WANN4，WELM4和WRBF4模型的准确性_min，H _s，W _s和RH输入变量在均方根误差（RMSE）降低方面分别约为56–41％，44–31％，57–46％，35–26％，27–16％和43–28％站。虽然混合模型（即WANN4，WELM4和WRBF4）对于ANN4，ELM4和RBF4分别提高了单个模型的精度约31–21％，23–18％和26–25％。