## 2 Methodology  
A systematic review [31, 32] of electronic resources was conducted to investigate the underlying electrical load forecasting models. Initially, different types of electrical load forecasting were searched and studied. Subsequently, the focus shifted to short-term electrical load forecasting. The initial search using the keyword \"short-term electrical load forecasting\" yielded 3165 results. Preliminary studies on STLF revealed key topics, including electric load demand and supply models, time-series models, exponential smoothing methods, regression models, machine learning models, expert systems, hybrid models, and smart grids. These results were further refined using the following criteria:  
* Objective: Short-term electrical load forecasting.
* Keywords: Short-term electrical load forecasting, time-series models, exponential smoothing methods, regression models, machine learning models, expert systems, hybrid models, and smart grids.
* Studies covering STLF supply/demand.
* Key novel/review publications on STLF models from renowned institutions/authors.
* This review exclusively incorporates research papers published in peer-reviewed journals, excluding contributions from conference papers.  
Out of 282 publications on load forecasting, 208 reviews on short-term load forecasting and demand planning, including smart grid applications, were selected for analysis. First, STLF models were categorized into statistical methods, intelligent-computation-based methods, and hybrid models. Based on this categorization of STLF models, a comprehensive study considering the contribution and performance accuracy of the reviewed papers is presented. Furthermore, the study is extended to include the role of STLF in smart grids and provides an inclusive review of STLF methodologies considering smart grid application. The results are postulated based on a comprehensive study of various state-of-the-art prediction models. Considering the influential factors to improve the performance of STLF methods, the research gaps and future directions are outlined. The methodology of the paper is presented in Figure 4. Table 1 lists relevant literature reviews of load forecasting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Review Type** & **Remarks** \\ \hline
[33] & 1982 & Systematic & Categorized modeling techniques reflect load demand. Multi-variable identification methods model load demand at key nodes in large power systems. \\ \hline
[34] & 1987 & Tutorial & Emphasized the critical role of STLF in EMS online scheduling and security operations and addressed practical implementation challenges. \\ \hline
[35] & 2001 & Methodological & Presented a comprehensive review of CI approaches to STLF in power plants without relying on traditional models. It includes eight case studies that demonstrate the effectiveness of the approach under different conditions. \\ \hline
[36] & 2001 & Critically evaluated & Evaluated skepticism of NNs in STLF in various publications, suggesting that more research on large NNs with stringent standards is needed for conclusive results. \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of relevant literature reviews of load forecasting.