# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
[7] used a linear regression model to forecast the monthly and yearly electricity consumption for the German market, taking into account some factors, especially the higher efficiency of electricity usage from year to year. [PERSON] et al. [8] proposed a nested combination of three subsections for modeling to reduce the forecasting error and can be used for several years ahead. [PERSON] [9] examined the correlation of actual load supply with factors, such as population, gross national product, consumer price index, and weather temperature of Iraq using linear logarithmic and ANN models. [PERSON] and [PERSON] [10] presented multiple Holt-Winters models to improve the forecasting electricity demand of the Spanish national electricity market for very short-term forecasting. [PERSON] [11] showed the superiority of triple seasonal methods of Holt-Winters and auto regressive moving average (ARMA) models for Short-Term Electricity Demand Forecasting. Recently, [PERSON] et al. [12] proposed a new method to initialize the level, seasonality, and trend in multiple seasonal Holt-Winters models. [PERSON] et al. [13] used a trigonometric grey model with a rolling mechanism and Holt-Winters smoothing method to predict long-term non-residential electricity consumption for ten years up to the year 2020 and showed low average deviation. [PERSON] et al. [14] employed Holt-Winters and Autoregressive Integrated Moving Average (ARIMA) models using real data from the year 1980 to 2011 to forecast the electricity consumption for Pakistan up to the year 2020 and showed better results for the Holt-Winters model than ARIMA model. [PERSON] et al. [15] employed a fuzzy logic model for long-term load forecasting of one year based on temperature and humidity in addition to historical load data. [PERSON] [16] developed a scenario-based model for the African power system using the Schwartz methodology in this context and showed an increase in electricity demand of 4% by 2040. [PERSON] et al. [17] proposed a least squares support vector machine optimized by two different models to forecast the demand of electric load in the multi-energy coupling mode. More examples can be found in several reviews carried out by [PERSON] et al. [3], [PERSON] et al. [4], [PERSON] et al. [5], [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] and [PERSON] [20], and [PERSON] et al. [21]. A relatively new method called Prophet is introduced by [PERSON] and [PERSON] [22] for forecasting, which has a high potential to be used in electric load forecasting. Different implementations were found for this model in recent years. For example, [PERSON] et al. [23] compared two models: ARIMA and Prophet in Bitcoin forecasting. The results show that Prophet had an accuracy of about 94.5%, which was much better than ARIMA, which had an accuracy of only 68%. In addition, [PERSON] [24] used three models: ARIMA, Prophet, and long-short term memory (LSTM) recurrent neural networks in forecasting the prices of cryptocurrencies: Bitcoin, Ethereum, and Litecoin. The results show that the application of LSTM and Prophet led to very high accuracy between 93% and 99% for the three currencies, while the accuracy of the ARIMA model ranges between 82% and 66% only. In addition, [PERSON] et al. [25] carried a comparative for heat short-term demand forecasting methods using the Autoregressive Model (ARM), Non-Autoregressive Model (NARM), and Prophet based on real data of an Italian utility company. The ARM was superior to the other models in short-term forecasting. A thorough recent study carried out by [PERSON] [26] where five different forecasting models (Simple Exponential Smoothing, ARIMA, Dynamic Harmonic Regression, Neural Network, and Prophet) were used for prediction of wind speed in two states in India (Tamil Nadu and Maharashtra). The neural network model provided the best results. However, the Prophet model showed promising results, and it was recommended to be used in the future. Different studies about long-term forecasting for Kuwait were carried out.