# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 4 Conclusions  
Long-term energy forecasting plays a pivotal role in providing insights for policymakers' decisions, such as the need to expand the electrical power utilities. In this study, two forecasting models were utilized for long-term maximum electrical load forecasting of Kuwait; the Prophet and Holt-Winters forecasting models. Both models performed outstandingly in three performance metrics: accuracy, generalization, and robustness and provided essential knowledge for planning as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Month/YY**} & \multicolumn{2}{c}{**Prophet**} & \multicolumn{2}{c}{**Holt–Winters**} \\ \cline{2-5}  & **Max** & **Upper 99\% CI** & **Max** & **Upper 99\% CI** \\ \hline Aug-20 & 14,727 & 15,887 & 15,758 & 17,292 \\ Sep-20 & 13,744 & 15,036 & 14,706 & 16,031 \\ Oct-20 & 10,951 & 11,813 & 11,718 & 12,552 \\ Nov-20 & 8771 & 9568 & 9384 & 10,313 \\ Dec-20 & 6930 & 7619 & 7415 & 8034 \\ Jan-21 & 6828 & 7534 & 7306 & 7823 \\ Feb-21 & 6866 & 7504 & 7347 & 7962 \\ Mar-21 & 8095 & 8684 & 8661 & 9424 \\ Apr-21 & 10,079 & 11,120 & 10,785 & 11,814 \\ May-21 & 12,821 & 13,873 & 13,719 & 14,743 \\ Jun-21 & 14,571 & 15,986 & 15,591 & 16,836 \\ Jul-21 & 14,899 & 16,263 & 15,942 & 17,436 \\ Aug-21 & 14,628 & 16,002 & 15,652 & 16,779 \\ Sep-21 & 14,173 & 15,180 & 15,165 & 16,363 \\ Oct-21 & 11,500 & 12,375 & 12,305 & 13,528 \\ Nov-21 & 9190 & 10,028 & 9834 & 10,861 \\ Dec-21 & 6972 & 7492 & 7460 & 8122 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the monthly maximum load of forecasted maximum peaks from the Prophet and Holt–Winters models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Actual** & **MEW [44]** & **Prophet** & **Max 99\% CI** & **Holt–Winters** & **Max 99\% CI** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the maximum load of actual data, forecasted maximum peaks from Ministry of Electricity and Water and the Prophet and Holt–Winters models.  
1. The Prophet model achieved excellent values for MAPE, MAE, RMSE, CVRMSE, and R\({}^{2}\) with values of 1.75%, 147.89, 205.64, 7.61, and 0.9942, respectively. The Holt-Winters model performance metrics were 4.17, 343.33, 475.76, 17.59%, and 0.9694 for MAPE, MAE, RMSE, CVRMSE, and R\({}^{2}\), respectively. The superiority of the Prophet model in these values indicates higher accuracy when compared to the Holt-Winters model.
2. The Prophet model exhibits better generalizability than Holt-Winters, as suggested by the simulated historical forecast investigation.
3. The robustness of the two models was assessed by adding white noise and found that the Prophet model is more robust than the Holt-Winters.
4. The forecasted maximum peak load reached 18,550 and 19,588 MW for the Prophet model and Holt-Winters model, respectively, by 2030. An additional capacity reservoir should be available to tolerate any failure or disruption of the electrical system.
5. The study suggests that the best months for scheduling the preventive maintenance for the years 2020 and 2021 are from November 2020 until March 2021 for both models.  
It is believed that the insights concluded from this study would be of assistance to policymakers in estimating the future electricity demands and the strategic development plan of the State of Kuwait in addition to the maintenance schedule.  
Conceptualization, A.I.A. and A.M.A.; methodology, A.M.A. and A.I.A.; software, A.M.A., validation, A.M.A., A.I.A., M.R.A., and S.N.A.; formal analysis, A.M.A. and A.I.A.; investigation, A.M.A. and A.I.A.; resources, A.M.A., A.I.A., A.S.A., M.R.A, and S.N.A.; data curation, A.I.A. and A.M.A.; writing--original draft preparation, A.I.A., A.M.A., A.S.A., M.R.A., and S.N.A.; writing--review and editing, A.I.A., A.M.A., A.S.A., M.R.A., and S.N.A.; visualization, A.I.A., A.M.A., A.S.A., M.R.A., and S.N.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.