## 2 Review of Related Work  
STLF is inherently challenging due to the non-linear, non-stationary, and noisy nature of electricity load time series, further complicated by multiple seasonal patterns. These complexities arise from diverse electrical loads influenced by weather, calendar factors, diversity of user behavior, and penetration of renewable energy solutions [13]. Due to their direct influence on the daily electricity dispatching that powers both residents' lives and social production activities, STLF and VSTLF have emerged as the central research domains within the field of power load forecasting [14]. Additionally, STLF plays a critical  
Figure 1: Summary of different types of LF with respective time horizons, domains, inputs, and outputs. role in guaranteeing the reliability of power systems, particularly during periods of scarcity or outage. Consequently, the development of accurate STLF methodologies is essential for effective energy system planning, which in turn contributes to the improvement of the country's economic growth [15]. Hence, STLF becomes the topic of this study. Moreover, accurately predicting electricity load necessitates careful consideration of diverse influencing factors, as demonstrated by the strong correlations observed between load demand and factors such as historical load data, calendar days, and weather patterns. In this context, STLF can be characterized as a dynamic, non-linear input and output mapping function that incorporates not only historical values but also a multitude of exogenous factors, such as weather conditions [16]. For this purpose, correlation analysis plays a pivotal role in analyzing the relationship between different factors and estimating their interdependencies. Pearson's correlation coefficient (PCC) analysis is a widely used and well-established method for this purpose [17; 18]. Substantial research has employed PCC analysis to investigate the interdependency between electrical load and weather variables in load forecasting, as evidenced by studies such as [19; 20; 21]. The selection of appropriate input variables can significantly expedite model development and enhance its forecasting accuracy [22]. Different forecasting methods possess inherent strengths and weaknesses, limiting any individual method's ability to achieve consistently exceptional performance across diverse scenarios. Motivated by the need to surpass the limitations of established forecasting methodologies, the research in electricity load forecasting models has witnessed a rising trend toward hybrid models. These models integrate popular techniques with modern evolutionary algorithms and expert systems, striving to achieve high prediction accuracy and retain interpretability simultaneously [23]. The popular ML methods used in load forecasting are tree-based, neural network-based, and SVM-based methods [24]. The SVM excels in generalization; however, its computational complexity remains high due to the quadratic programming involving inequality constraints. In response, LSSVM offers an improved solution by transforming these constraints into equalities, simplifying the optimization problem [25]. This significantly reduces the computational burden, making LSSVM a more attractive choice for applications in electricity load forecasting [22; 26; 27]. Furthermore, the inherent influence of randomly selected internal parameters on LSSVM performance necessitates the exploration of alternative optimization algorithms to enhance its learning and generalization ability. Evolutionary algorithms, inspired by natural selection, hold promise in identifying optimal parameter configurations for SVM, potentially leading to enhanced forecasting accuracy [28]. Among evolutionary algorithms, the BFOA garners significant recognition for its unique strengths. It excels in simultaneously exploring a vast search space (encompassing both global and local optima) and leveraging a multi-center approach [29].