Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

This study models solar irradiance at six stations in Iran and the USA on an hourly scale. We explored two seasonal emotional artificial neural networks (EANN): sequence-EANN (SEANN) and wavelet EANN (WEANN). Analyzing ten years of climatic and solar data, we evaluated uncertainty using prediction intervals (PIs) computed via the bootstrap method based on artificial neural networks (ANNs). Unlike standalone EANNs, the proposed seasonal models effectively captured seasonal information and leveraged time series processing advantages. Utilizing Wavelet and Fourier transforms, these models captured long-short autoregressive dependencies in solar irradiance, addressing extended seasonal dependencies. Results showed that the seasonal EANN models outperformed the classic EANN model by approximately 15 % and the classic feed-forward neural network (FFNN) by about 25 % in both training and testing. The WEANN model demonstrated the highest performance in PIs, with an average normalized mean PI width (NMPIW) of 0.8 and an average PI coverage probability (PICP) of 0.96.