A novel global solar exposure forecasting model based on air temperature: Designing a new multi-processing ensemble deep learning paradigm

A novel global solar exposure forecasting model based on air temperature: Designing a new multi-processing ensemble deep learning paradigm

The total quantity of solar energy falling on a horizontal plane surface is the global solar exposure (GSE, i.e., total solar energy). Precise forecasting of GSE is important in many fields such as renewable energy, agriculture, and public health, particularly by the limited hydro-meteorological time series information. This research aims to develop an advanced multi-processing deep learning (DL) paradigm to forecast weekly GSE based on maximum (Tmax) and minimum (Tmin) air temperatures as the drivers at Brisbane and Perth airport stations in eastern and western Australia during 2000 to 2022. The proposed model was comprised of an extra tree feature selection (FS) integrated with two novel decomposition techniques, namely time-varying filtering-based empirical mode decomposition (TVF-EMD), and empirical wavelet transform (EWT), and a powerful ensemble deep random vector functional link (ED-RVFL) approach. To validate the main model, the RVFL, bidirectional long-short term memory (Bi-LSTM), and bagged regression tree (Bagging) machine learning (ML) models were examined in hybrid and standalone counterpart frameworks. First, the extra-tree FS determined the significant lags of the predictors based on an importance benchmark criterion. Then, by applying the optimal gained lags to the feeding models, all of the original predictors were decomposed using TVF-EMD and EWT univariate feature extraction. The final forecast was computed by aggregating all the individual forecasts of the intrinsic mode functions (IMFs) and residual components. In addition, eight statistical indicators (including coefficient of correlation: R, root mean square error: RMSE, Kling-Gupta efficiency: KGE, index of agreement: IA, uncertainty coefficient with 95% confidence level: U95%, mean absolute percent error: MAPE, Nash-Sutcliffe efficiency: NSE, and mean absolute error: MAE) and several graphical methods were utilized to evaluate the performances of the models. The modeling results indicated that the ED-RVFL-TVF-EMD (R = 0.9665, RMSE = 1.9193 MJ/m2, and KGE = 0.9565 for the Perth airport station) and ED-RVFL-EWT (R = 0.9218, RMSE = 1.9708 MJ/m2, and KGE = 0.8552 for the Brisbane station) outperformed all other models, followed by RVFL, Bi-LSTM, and Bagging in a hybrid format. With the high predictive robustness, both decomposition-based frameworks can be useful for solving energy forecasting problems. The new modeling approach developed in this study can provide more precise forecasts for decision-makers to better address climate change, agriculture, and energy crises.

File reproduced in accordance with the copyright policy of the publisher/author.