*5.5. Results Discussion*

The lowest obtained year-ahead UCLF forecasting errors from each technique are summarized in Table 9. These results show that the lowest UCLF forecasting errors were obtained by the ensemble model. The ensemble model was then followed by the LSTM-RNN, DBN, and then OP-ELM. The two deep learning techniques, thus, achieved higher accuracies than the non-deep learning technique, OP-ELM. It was observed that with all techniques, apart from OP-ELM, the lowest errors were attained in Experiment 1. Experiments 4 and 5 showed a sharp increase in errors, relative to the rest of the experiments with all the techniques. Thus, the exclusion of the installed capacity as an input variable decreased the accuracy of the models of the techniques used. The plots of the target UCLF and the year-ahead forecasted UCLF for the models with the lowest errors per technique are presented in Figures 8–11. These plots are plotted for the period of 1 January 2019 to 31 December 2019. Each plot of the individual models also includes the ensemble model with the lowest forecasting error. The plots of the UCLF forecast by the models that make up the ensemble model are plotted in Figure 9.


**Table 9.** Summary of lowest obtained errors per used technique.

**Figure 8.** A plot of the OP-ELM and ensemble lowest error model year-ahead UCLF forecast against the target UCLF.

**Figure 9.** A plot of the LSTM-RNN and ensemble lowest error model year-ahead UCLF forecast against the target UCLF.

**Figure 10.** A plot of the DBN and ensemble lowest error model year-ahead UCLF forecast against the target UCLF.

**Figure 11.** A plot of the ensemble lowest error model and the two aggregated models' year-ahead UCLF forecast against the target UCLF.
