## 1 Introduction
[PERSON] and [PERSON] [15] implemented a collaborative principal component analysis and fuzzy feed-forward neural network (PCA-FFNN) methodology in addition to the partial-consensus fuzzy intersection and radial basis function network (PCFI-RBF) approach to forecast the electrical loads of Taiwan. [PERSON] et al. [16] presented a methodology that associates hierarchical models with the bottom-up approach and the inclusion of the Bayesian inference to develop forecasting of electricity consumption for the pulp and paper industry in Brazil ranging from 2015 until 2050. A relatively new methodology is introduced by researchers from Facebook called Prophet model [17]. It is a simple yet robust for estimation because of its structure of adjusting parameters without investigating the original model's details. It contains a decomposable time series model with three main model components: trend, holidays, and seasonality. In a recent study [18], the Prophet model outperformed the well-established Holt-Winters model in Kuwait's long-term peak load forecasting. The use of this method in forecasting is expected to spread due to its robustness and accuracy. On the other hand, recent advances are employed on the Holt-Winters model [19], which probably improves its accuracy. A recent study by [PERSON] et al. [20] predicted the electricity consumption of a city in China by proposing a hybrid forecasting model and using the fruit fly optimization algorithm to select the optimum smoothing parameters. One of this study's main contributions is to explore the use of the Prophet forecasting model, with single and multi-regressors, and the multi-seasonality Holt-Winters model for the long-termforecasting of electricity generation. The dataset is based on the State of Kuwait as a case study with a historical dataset from January 2015 to May 2020. Kuwait is a country in the Middle East, and its economy is dependent on oil. The major commodities in Kuwait, such as electricity, are supported financially and owned by the government, aiming to keep them as low as possible for customers. Kuwait has eight conventional power plants located in different cities with a total installed 19,673 megawatts [21]. Electricity peak demand in Kuwait varied dramatically in the last 70 years with a generally decreasing trend. The average annual increase in the fifties was around 32%, then it decreased to 26% in the sixties to 15% in the seventies and reached around 8% in the nineties. The annual increase rate has decreased during the last twenty years and reached around 3.68% in 2019 [22; 23; 24]. There is a dramatic increase in the population and consumption per capita, as depicted in Table 1 below. The increase in electricity generation is attributed to population growth and economic development [25]. The annual energy use per capita in 2018 is 14,235 kWh, which is considered very high and among the world's highest. [21; 22; 23; 24; 26]. The average household in Kuwait consumed is reported [27] in 2014 to be about 38 MWh, which is the highest globally. The severe hot weather in summer and historically low prices are considered the main reasons for this increase. However, other factors, such as people's lifestyles, inefficient construction practices, and installed equipment, might also contribute [26]. One of the main points proposed [28] for fixing the high generation's need is to tackle the subsidized energy prices, which inspire greater electricity. Another viable idea is the use of dynamic pricing [29], which can change the pattern or reduce the quantity of people's electricity consumption. Increasing electricity tariffs is expected to be a significant contributor to change the consumption pattern of people. However, there are some difficulties in implementing these reforms due to their political nature [28]. [PERSON] [30] used energy audit techniques and implemented non-retrofitting and retrofitting on a two-story educational facility that results in more than 50% annual total saving for governmental buildings.