# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 3 Results and Discussion
### Future Peak Loads Forecasting  
According to the assessment of the two models, the Prophet model outperforms the Holt-Winters model and, therefore, should provide more reliable forecasting. The real data of daily peak loads consumption from 2010 to the end of May 2020 are used to forecast the daily peak load from June \begin{table}
\begin{tabular}{c c c} \hline \hline
**Noise Intensity** & **Prophet** & **Holt-Winter** \\ \hline
0\% & 0.9942 & 0.9694 \\
20\% & 0.9892 & 0.9483 \\
40\% & 0.9795 & 0.9275 \\
60\% & 0.9708 & 0.9014 \\
80\% & 0.9604 & 0.8883 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy reduction of different models under various noise intensities. Figure 10: Variations of the coefficient of determination at different noise intensities for both models. 2020 to 31-12-2030, with results plotted in Figures 4 and 5. The forecasting plots also provide essential information about the yearly maximum peak demand. Estimating the maximum peak demand is crucial for long-term strategic decisions in terms of electricity capacity expansion plans to withstand the foreseen maximum loads. [PERSON] et al. [40] reported that the electric peak demand is expected to reach 30,000 MW by 2030. [PERSON] and [PERSON] [30] applied the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods to forecast the peak loads for Kuwait in three scenarios according to the expected growth rates of the gross domestic product (GDP) for the years 2014-2024. The three sceneries were associated with high, average, and low growth of GDP. The high, average, and low growth of GDP models were increasing in different rates reaching maximum forecasted loads of 18,700, 27,300, and 44,500 MW, respectively, for the year 2024. [PERSON] et al. [41] showed that the expected demand in Kuwait is increasing by an average of 2.8% annually by 2030, and the installed capacity should be higher than 21.8 GW. Past studies [30; 40; 41; 42; 43] about the maximum peak loads forecasting showed a clear increasing trend for peak loads in the coming years, reaching about 30 GW by 2030. On the other hand, the recent study and the new forecast of MEW show a similar trend but lower in magnitude. The long-term forecasting provides daily, monthly, and annual maximum peaks. Table 3 presents a comparison between actual maximum peak load data and forecasted demand from the MEW in different years (2017-2019) [42; 43; 44], Prophet, and Holt-Winters models. The MEW forecasting showed variations in the percentage increase in annual maximum load from 2017 to 2019 with a decreasing trend. In the year 2017, there was a yearly increase in the maximum peak load by an average of 5.4%. The average increase was reduced in the forecast for 2018 to reach 4.7% annually. However, the growth became more conservative in the statistical book of the year 2019 and reached 1% only. The forecasting for years between 2020 and 2030 by the Prophet and Holt-Winters models showed an increasing trend with varying percentages for each year, reaching a maximum peak load of 18,550 and 19,588 MW by the year 2030, respectively. The magnitude of the increase in the Prophet model is between the value of the maximum peaks of MEW in the year 2019 and the Holt-Winters model that suggests the Prophet model is more reliable when compared with the actual data. It is a common practice to have a capacity reservoir to withstand any sharp increase in the daily consumption of electricity. Accordingly, the 99% confidence interval can be used to be the capacity reservoir. As a result, the capacity reservoir should reach up to 22.1 and 23.3 GW for the Prophet and Holt-Winters models to tolerate any failure or disruption of the electrical system during the maximum peak load time by 2030. On the other hand, Table 4 presents the monthly forecasted maximum load peaks for 17 months from August 2020 to December 2021. The data provided in Table 4 are essential for monthly planning in the powerplants.