## 1 Introduction  
Climate change prompts us to adopt decarbonization measures that include using renewable energy, improving energy efficiency, and optimizing energy use [1, 2]. These measures are essential for achieving the climate goals established in the Paris Agreement, specifically, reducing global greenhouse gas (GHG) emissions to limit the increase in global temperature to a maximum of 2 \({}^{\circ}\)C [3]. The inclusion of renewable energy sources and effective energy management takes place within energy systems, which generally consist of electrical loads that must be met by electrical generation. These electrical loads can range from just a few watts for an LED spotlight, several kilowatts for a house, tens of megawatts for a small city, a few gigawatts for a metropolis, to hundreds of gigawatts for industrialized countries. A power system, such as an electrical grid, consists of interconnected electrical components that generate, transmit, distribute, and supply electrical energy. A well-functioning system requires extensive analyses, the most critical being an understanding of the electrical load it must meet through its various generators. Therefore, electrical load forecasting is essential for power systems, influencing everything from design and installation to operation and maintenance [4]. The dynamics of electric load demand are a complex issue influenced by various local factors, including the type of day, weather conditions, and economic, social, and political elements. To model load demand, representative load curves (RLCs) are used to evaluate demand on a daily, weekly, monthly, or annual basis [5; 6]. For instance, in daily analysis, load demand fluctuations can be influenced by the type and time of day, temperature, rainfall, levels of industrial and agricultural activities, and social factors such as holidays and festivals. Accurate energy load forecasting plays a critical role in the efficient operation of electrical grids, influencing decisions related to energy generation, load dispatch, infrastructure development, and maintenance scheduling [7]. For this reason, load forecasting has become a fundamental topic in our lives, and there is significant scientific activity focused on developing more accurate load forecasting methodologies at all time resolutions [8; 9]. Load forecasting for utilities across various time horizons helps ensure the safety and stability of the power supply. Accurate load forecasting to maximize electrical power utilization contributes to cost savings and reductions in greenhouse gas emissions, as well as to the finance, infrastructure, operation, and maintenance of the grid. Indeed, a load prediction with a 1% error could result in operating costs amounting to millions of dollars per year [10; 11]. The effectiveness of load demand forecasting relies on the forecast horizon, which corresponds to the time scale in the future for which the forecasts are made. The forecast horizons relate to the corresponding duration for which the forecasts are conducted, along with the frequency of data utilized for the forecast. The forecast horizon is defined as \"the length of time into the future for which forecasts are to be prepared\", in alignment with the definition from the statistical office of the European Union [12]. Table 1 summarizes the forecast horizons, types of forecasts, and their typical applications in electric power systems [13; 14; 15]. Based on the time horizon, forecasts can be categorized into different types--real-time, very short-term, short-term, medium-term, and long-term--though there is no international consensus on this issue. Furthermore, various applications in electric power systems employ different types of load demand forecasts depending on the speed at which their data and information processes vary. As power systems become more complex and renewable energy sources are integrated, developing robust short-term load forecasting (STLF) models is essential for ensuring grid reliability and stability [16].