## 1 Introduction  
Energy is a cornerstone of sustainable development for nations worldwide. Over the past decade, there has been an increase in global energy demand. Malaysia, among the most highly developed states of the Southeast Asia region, has projected its total final energy consumption to almost double by 2050, driven by an increasing urban population and economic growth. The energy demand in Malaysia has correlated with gross domestic product (GDP) growth as the economy depends on energy-intensive industries such as manufacturing. However, the previous COVID-19 pandemic's impact disrupted the energy demand, which has lowered the GDP growth. In 2020, Malaysia's GDP experienced a significant decline of 5.54%, leading to a corresponding decrease in electricity generation (2.4%) and total final energy consumption (\(-0.5\%\)), particularly within the industrial sector compared to 2019. Therefore, the government has planned an economic recovery program to stimulate the economy, with an expected GDP growth of 3.44% per year from 2020 until 2030 [1]. The growth illustrates the increasing energy demand needed to achieve the goal. Malaysia's energy landscape is projected to undergo a significant transformation, balancing anticipated demand growth with a resolute transition towards renewable energy (RE) and energy efficiency. The electricity utility in Peninsular Malaysia has set an ambitious target of achieving 20% RE capacity and attaining net-zero emissions by 2050. This goal will be pursued through a sustainable pathway that aims to reduce the emission intensity and halve coal generation capacity by 2035 [2]. The exponential growth and innovation in RE are actively shaping a more interconnected and environmentally sustainable global energy future. Accurate electricity forecasting serves a pivotal role in accelerating this transition, offering precise insights into future energy demand and facilitating optimized generation strategies that minimize environmental impact and maximize sustainability [3]. Based on the literature, electricity load forecasting has a different classification of forecasting depending on the time spans, such as very short-term load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF) [4]. VSTL forecasts electricity demand in minutes to hours [5]. It helps manage building energy systems with features like energy storage and peak load response [6]. The STLF covers a time frame of hours to days. This helps power companies make daily decisions like spinning reserve control, optimal unit commitment, and scheduling preventive maintenance [7]. The MTLF forecasts load for periods of days to months. It is valuable for anticipating seasonal demand variations and informing critical decisions related to fuel purchases, maintenance planning, and utility assessments [8]. The LTLF extends beyond one year and reaches out to two decades. It plays a critical role in strategic planning for the power sector, guiding decisions on constructing new generation capacity and shaping the overall landscape of power supply and delivery systems [9]. Figure 1 summarizes the different forecasting horizons (VSTLF, STLF, MTLF, and LTLF) used in power system applications. Each horizon is characterized by its domain (application area), specific input data requirements, and the corresponding output metrics employed for model evaluation. The figure further categorizes load forecasting methods into three main types: traditional methods, machine learning methods, and hybrid methods. Autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA), are traditional time series methods. In contrast, support vector machines (SVM) and artificial neural networks (ANN) are types of machine learning (ML). A hybrid method, combining a few individual models, can potentially enhance forecasting accuracy. Least square support vector machines (LSSVM) have been widely used in the realm of load forecasting and have achieved good forecasting results.