## 1 Introduction  
Global electricity demand is projected to reach 26.8 kTWh by 2025, driven by population growth, urbanization, and economic expansion [1], as shown in Figure 1. Diverse resources such as water, wind, solar, fossil fuel, thermal, and nuclear power contribute to electricity generation. This increasing demand is driving up production costs. An efficient energy management system (EMS) relies on overcoming the challenges of generation, transmission, and distribution challenges. The electric grid (EG), consisting of power plants, substations, and transmission and distribution lines, connects suppliers to consumers [2]. Overloading the grid can lead to power quality issues, requiring the installation of new power plants. However, existing grids lack accurate forecasting mechanisms to predict the causes of outages and response times, creating challenges for resource allocation [3, 4, 5, 6]. Despite increasing demand due to population growth, the current electrical power system (PS) has changed little [7; 8; 9]. It suffers from visibility issues, slow mechanical switches, inadequate monitoring, and poor power management. Climate change, component failures, increasing energy demand, population growth, and fossil fuel consumption require modern grid technology. Smart grids and microgrids have emerged as critical next-generation energy infrastructures to address these challenges [10; 11]. Modern smart grids with advanced metering enable fast two-way communication, optimize energy consumption, and improve grid efficiency through effective load management. They also have faster self-healing capabilities and greater resilience to disasters and cyber threats. In addition, smart grids quickly identify and resolve problems with minimal customer impact, while conventional systems are slow to respond. Finally, power flow regulation in conventional grids is severely limited compared to smart grids and microgrids [12]. Integrating distributed energy resources (DERs) address the growing demand for electricity while complicating power systems [13]. Accurate load forecasting (LF) is critical for effective grid management, especially with increasing DER integration. Emissions targets require widespread integration of renewable energy sources (RESs), as shown in the total electricity generation forecast in Figure 2. Additionally, the global trend in RES electricity generation (%) is shown in Figure 3[1]. Reliable load data, facilitated by the Internet of Things (IoT), are essential for accurate LF. Researchers are developing innovative approaches to predict future electricity demand in modern grids [14]. The LF guides critical decisions such as dispatch, unit commitment, and fuel allocation [15]. It minimizes the gap between power production and demand, thereby reducing generation costs. Rapid changes in load consumption patterns require models that can adapt and learn from historical data. However, model validation is affected by boundary conditions and data quality issues, resulting in case-by-case adjustments. A model that lacks essential features can result in significant operational, infrastructure, financial, or maintenance losses. A reduction of only 1% in the mean absolute percentage error in load forecasting can have a significant impact on the generation side, potentially reducing generation costs by 0.1% to 0.3%, for a consequential impact of 3-5% [16]. Figure 1: Electricity consumption trend 2000–2050. Figure 2: Electricity generation by energy source 2050 (%). In the literature, electrical load forecasting is categorized into long-term load forecasting (LTLF), medium-term load forecasting (MTLF), short-term load forecasting (STLF), and very short-term load forecasting (VSTLF), which are discussed in detail in Section 3. However, the focus of this paper is on STLF and its associated forecasting techniques due to its importance in power utility management. STLF is critical to the efficient operation of conventional power systems, influencing dispatch plans [17], demand-side management, and generation monitoring [18].