## 1 Introduction  
The accurate prediction of future energy demand enables better planning and operation for electricity providers, allowing them to prepare adequately and avoid issues of overproduction or underproduction. This not only reduces costs but also increases the efficiency of the energy industry. Electricity demand can be predicted using two primary methods: physical model-based approaches and data-driven techniques. Physical models are easy to understand and implement. However, the high computational cost required can make real-time forecasting or large-scale simulations impractical in some cases [1]. Furthermore, aggregating multiple physical models, each with inherent uncertainties, can decrease the overall accuracy of the forecast, especially for large-scale prediction problems [2]. Data-driven techniques are further categorized into statistical and machine learning methods. Statistical methods often utilize a regression analysis or time series prediction. Regression analysis involves creating mathematical models that define the relationships between independent and dependent variables to forecast electricity demand. For example, an enhanced multivariable linear regression model has been employed to predict daily average cooling loads in office buildings [3]. Time series analysis, another statistical method, was used to handle dynamic data for prediction purposes. Although these statistical methods are straightforward, they can struggle with complex interactions between input variables, leading to lower calculation efficiency and accuracy [4]. Machine learning and data mining techniques have attracted much attention worldwide for both short-term and long-term electricity demand forecasting. Many deep learning models have been employed to predict electricity demand or load, such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM). These models can independently learn and predict electricity demand by scrutinizing energy usage patterns, effectively addressing the nonlinear problem [5]. Reference [6] presented a short-term load prediction model based on a DNN and the stacked ensemble method. In the study of [7], the authors applied a multiple regression model alongside three CNNs to forecast Florida's future electricity usage. The multiple regression model results indicated that the variable month, cooling degree days (CDDs), heating degree days (HDDs), and gross domestic product (GDP) were significant predictors of future electricity demand. Reference [8] developed two regression models--ordinal and multiple linear regression--to predict electricity demand in Greece, utilizing seventeen years of historical data that included economic, weather, and energy efficiency variables. This study concluded that the GDP had the most significant influence on electricity demand, with HDDs and CDDs also playing important roles. The ordinal model achieved a Mean Absolute Percentage Error (MAPE) of 0.74%. Another study examined the impact of climate variables on electricity demand in Sydney, Australia, using a backward selection multiple regression model. Ten years of historical data were analyzed, and the model produced an R-square value of 0.816, identifying the CDD, wind speed, evaporation, and humidity as significant variables [9]. Reference [10] also applied multiple linear regression to forecast hourly and monthly electricity demand in a region of Australia, identifying the CDD, HDD, humidity, and the number of rainy days as key predictors. Many data-driven methods have been used for long-term load forecasting for strategic power system planning. Conventionally, long-term load forecasting has relied on Artificial Neural Networks (ANNs) or regression-based methods, utilizing extensive historical data on electricity load, weather, economic factors, and population. However, these classical approaches have limitations, such as insensitivity to changing trends over long periods and difficulty in managing a large number of variables and complex relationships.