# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models  
[PERSON]  
1 Department of Manufacturing Engineering Technology, College of Technological Studies, P.A.A.E.T., P.O. Box 42325, Shuwaikh 70654, Kuwait; [EMAIL_ADDRESS]
2 Department of Electronics Engineering Technology, College of Technological Studies, P.A.A.E.T., P.O. Box 42325, Shuwaikh 70654, Kuwait; [EMAIL_ADDRESS] (A.M.A. ); [EMAIL_ADDRESS] (M.R.A.) [PERSON]  
2 Department of Electronics Engineering Technology, College of Technological Studies, P.A.A.E.T., P.O. Box 42325, Shuwaikh 70654, Kuwait; [EMAIL_ADDRESS] (A.M.A. ); [EMAIL_ADDRESS] (M.R.A.) [PERSON]  
2 Department of Electronics Engineering Technology, College of Technological Studies, P.A.A.E.T., P.O. Box 42325, Shuwaikh 70654, Kuwait; am. are traditionally categorized into short-term, medium-term, and long-term forecasting [2; 3; 4; 5; 6]. First, the short-term forecasting investigates the sub-hourly, hourly, daily, and weekly predictions. Second, the medium-term forecasting that includes weekly, monthly, and quarterly forecasting. Lastly, long-term forecasting contains periods of a year or beyond. Each one of these planning horizons is important; however, the long-term forecasting can be considered the most critical horizon due to the consequences of the strategic and costly decisions, such as the expansion of utility plants. An overestimation of electricity demand, especially in the long-term forecasting, will result in a significant increase in the construction of unnecessary electricity generation plants. In contrast, an underestimation of electricity demand forecasting will result in a shortage of electricity production and customer dissatisfaction. Therefore, the study of this research will focus on long-term electricity demand forecasting and the use of models that give the most accurate predictions. An enormous number of research in electrical load forecasting is found in the literature that tackles the different challenges faced by the power industry and provides a superior forecasting method for predicting the demand of the electrical load. Various types of models and methodologies are implemented for electrical load forecasting in the literature with several parameters in a range of complexity degrees to achieve the best load forecasting accuracy. The methods can be categorized into several groups, but for simplicity, it can be divided into two main groups, namely the group of conventional models and the group of artificial intelligence (AI) models [1; 2; 3; 4; 5; 6]. Conventional methods include models, such as regression models, time series models, exponential smoothing, and gray models [2; 4]. In contrast, artificial intelligence models include models such as artificial neural network (ANN)-based models, support vector regression (SVR), genetic algorithm (GA) models, machine learning (ML) models, and deep learning (DL) models [1; 2; 3; 4]. These models were used as simple or hybrid models or as a combination of more than one method, whether conventional or artificial intelligence models. Generally, the artificial intelligence models are more complex than conventional models. During the last two decades, the application of the AI models in the forecasting processes was rapidly increasing compared to the conventional models, and that can be attributed to the development of different AI models and the advancement of the computers [2]. However, a relatively recent review [3] depicted that the conventional methods, such as regression and/or multiple regression, are still widely efficiently used, especially for long-term forecasting despite their simplicity. Researchers globally tackled the long-term forecasting challenges by using different models and methodologies to achieve superior and accurate forecasting. For example, [PERSON] et al.