## 2 Methodologies  
The electrical generation's real data between January 2015 and July 2020 was attained from the Ministry of Electricity and Water (MEW) is used in this research. The data is plotted in Figure 1, showing the daily generation for approximately six years. A vivid seasonality of the data is observed with an annual maximum generation in the hot summer, especially June to August. There is an increasing trend of the generation, as noticed from Figure 1, due to the growth of population, the building of new cities, as well as economic development, and this is expected to continue in the upcoming years.  
The data consists of five years with daily observations for electrical generation from January 2015 to May 2020 with 1978 data points. Taking a closer look at Figure 1, a yearly seasonality occurring within July-August, can be observed. During these months, the high electricity demand is due to Kuwait's hot summer, with temperatures reaching 55 \({}^{\circ}\)C. The data also reveals a weekly seasonality with low demand on Fridays and Saturdays each week due to the factories' and industries' closure on weekends. Furthermore, the third seasonality corresponds to the year's meteorological seasons and will be addressed hereafter as quarters to avoid the confusion with methods' seasonality patterns.  
Figure 1: The actual data of generated daily loads data in Kuwait between January 2015–May 2020.  
In this study, long-term forecasting for electrical generation for the coming ten years is aimed. Therefore, a search for models that comprise data with time-series nature with different seasonality resulted in choosing the relatively new Prophet model from Facebook and the Holt-Winters model for this work.  
The relatively new Prophet model possesses some features such as tunable parameters by an expert during the analysis, which allows for fine-tuning and the ability to reach robust forecasting. The model's automatic tuning does not require a full understanding of the underlying model, making it easier to use. Limited studies are reported to use this model and proved to achieve vigorous results [18; 38]. Whereas the Holt-Winters model, with different variations, is used extensively for long-term forecasting and providing excellent results [39; 40].  
One of the Prophet model's points of superiority is its ability to deal with missing data and reach outstanding results. The Prophet model's flexibility for complex data can be achieved using an analyst-in-the-loop feature by integrating multiple-seasonality of different periods. The availability of single and multivariate forecasting in a straightforward manner adds a new strength for the Prophet model. On the other hand, Holt-Winters deals only with univariate forecasting. There exist different Holt-Winters model variations in the literature where it was generalized to include double and triple seasonalities [41; 42]. The Holt-Winter model was then generalized and introduced in literature to include n-seasonalities and was therefore known as nHWT [38]. Initialization methods of nHWT were proven to have a crucial impact on the accuracy of the forecast [19].  
In this research, the multivariate Prophet forecasting model is utilized to forecast the annual load generation needed in Kuwait for the upcoming 10 years and is compared with the univariate nHWT forecasting model with initialization methods. In the next sections, the implementation of the various Prophet and Holt-Winters forecasting methods is presented in detail.