**7. Limitations of the Study and Future Work**

This section presents the limitation of this study. As with most research, not all research-related aspects can be covered in a single study. As mentioned in Section 1, the study of South African UCLF behavior and UCLF forecasting is a new research area. This study does not focus on the speed of training the models, but rather on how well the models forecast the UCLF. Future work can include looking at the model training performance from the training speed perspective. The study forecast period is a year. This period was selected as it gives a wide enough window for the utility, at a daily resolution, to understand the UCLF behavior for the year. This understanding allows the utility company to plan over the year. The study does not research the performance of the models in shorter-term forecast windows, e.g., hourly, daily, weekly, etc. The performance of the models can, in the future, be studied for different forecast windows. Future research work should also consider looking at recent state-of-the-art techniques, such as temporal convolutional networks (TCN), gated recurrent units (GRU), and quasi-recurrent neural networks (QRNN). Given the performance of the equally weighted ensemble techniques in this paper, weighted ensemble techniques should be considered in future work. This future work can also investigate the ensemble models' performance when combining more than two models. Other benchmark techniques, such as naïve and multilayer perceptron, can be considered in future work.

**Author Contributions:** Conceptualization, S.M.; methodology, S.M.; software, S.M.; validation, S.M.; formal analysis, S.M.; investigation, S.M.; resources, S.M., A.N.H. and T.S.; data curation, S.M.; writing—original draft preparation, S.M.; writing—review and editing, S.M., A.N.H. and T.S.; visualization, S.M.; supervision, A.N.H. and T.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding. The authors would like to acknowledge the University of Johannesburg's Global Excellence and Stature (GES) 4.0 for funding of this research.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is not available on any public platform.

**Conflicts of Interest:** The authors declare no conflict of interest.
