## 1 Introduction  
Accurate electrical generation forecasting is essential for the management and policymakers of local and national power plants. Electric power generation is one of the complex processes where many parameters are involved and need to be optimized to deliver the electricity continuously and efficiently to the whole population [1]. On the other hand, power generation is faced with many challenges, such as environmental legislation, fluctuations in fuel prices, and the need to optimize the available resources. Moreover, different sources for power generation are used nowadays. These sources include nuclear energy, fossil fuel such as coal, natural gas, crude oil, other gasses, and renewable energy such as hydropower, solar, wind, municipal solid waste, and geothermal. Therefore, appropriate forecasting is a need for these power plants in order to support continued prosperity. Forecasting horizons vary from short-term to mid-term and long-term horizons [2; 3; 4; 5; 6]. Long-term forecasting is the most complex and critical due to the different parameters involved [6; 7]. Parameters include population, gross domestic product (GDP), consumer behavior, the advancement of technology, and weather temperature. Long-term forecasting is facilitated to predict the future maximum peak loads and/or annual demand and generation [3]. The forecast of maximum peak loads helps make significant decisions such as constructing new power plants and/or any expansion. On the other hand, forecasting the annual electrical generation provides critical data to define future power needs and assess logistics, such as fuel and personnel needed for the specified generation. Accurate forecasting guarantees efficient resource planning for the growing population and increasing demand for electricity that avoids over or underestimating the procurement of resources such as fuel. Different kinds of approaches are employed for electrical load demand forecasting in the literature with several parameters in various complexity degrees to attain the best electricity demand forecasting accuracy. The methods can be categorized into two main categories: conventional models and artificial intelligence (AI) models [2; 3; 4; 5; 6]. Conventional methods include time series models, exponential smoothing, regression models, and gray models [2; 3; 8]. In contrast, artificial intelligence models include models such as machine learning (ML) models, deep learning (DL) models, genetic algorithm (GA) models, artificial neural networks (ANN) based models, and support vector regression (SVR) [2; 3; 8]. Various studies investigated the long-term forecasting of electricity demand from different angles using multiples approaches. [PERSON] and [PERSON] [9] developed long-term forecasting for electricity demand in Spain until 2030, based on a simple growth decomposition scheme to identify other key factors. [PERSON] et al. [10] used a fuzzy logic approach to forecast Brazil's long-term annual electricity demand and extract rules from the input variables. [PERSON] and [PERSON] [11] decomposed the total electricity residential consumption in Brazil into three variables: the number of households, the average consumption of customers, and the electrification rate, and found the forecast by the product of the forecast of the three variables. [PERSON] and [PERSON] [12] used multiple linear regression analyses to investigate the electricity consumption correlation of GDP, population, and the average price of electricity during the period 1965-1999 of New Zealand. [PERSON] and [PERSON] [13] developed an artificial neural network (ANN) and optimized regression models with improved particle swarm optimization (IPSO) to forecast electrical energy consumption of Iran and the U.S. for 2010-2030. [PERSON] et al. [14] presented different regression models based on co-integrated or static data using historical data from 1970 to 2007 to develop a long-term consumption forecasting model for Italy.