**6. Conclusions**

This paper contributed to the body of knowledge about South African UCLF forecasting. (i) A novel study of the South African UCLF behavior using state-of-the-art AI (deep learning and ensemble) techniques was presented. LSTM-RNN, DBN, OP-ELM, and ensembles of these three techniques' models were investigated in South African UCLF forecasting. (ii) An investigation of the impact of the installed capacity, historic demand, and PCLF on the UCLF forecasting accuracy was presented. It was found that the installed capacity had the biggest impact on the UCLF forecasting error, with the exclusion of this variable doubling the errors with the respective techniques used. (iii) A novel deep-learning ensemble total South African UCLF forecasting system was introduced. It was found that

an ensemble of LSTM models achieved the lowest errors with an sMAPE of 6.43%, MAE of 7.36%, and RMSE of 9.21%. The lowest achieved LSTM model UCLF forecast errors were an sMAPE of 7.95%, MAE of 9.14%, and RMSE of 11.42%. The lowest achieved DBN model UCLF forecast errors were an sMAPE of 9.74%, MAE of 11.52%, and RMSE of 13.74%. The lowest achieved OP-ELM model UCLF forecast errors were an sMAPE of 10.21%, MAE of 11.57%, and RMSE of 14.65%. The lowest attained error was, thus, given by the ensemble model, followed by LSTM-RNN. The non-deep learning techniques' lowest achieved error was higher than that of the lowest errors achieved by the other techniques. Thus, ensemble deep learning techniques can be used to effectively forecast the total South African UCLF and, thus, load shedding.
