## 3 Load Forecasting  
Load forecasting is critical for demand forecasting, power industry calculations, and non-power sector planning. Table 2 outlines the advantages of traditional and modern grid forecasting and their impact on beneficiaries. However, for reliable LF, a thorough examination of load data dynamics is essential [43]. Key elements that directly and indirectly affect LF accuracy must be carefully considered during model development. Researchers must identify relevant factors for accurate prediction of electrical consumption patterns. Table 3 briefly describes key elements that influence LF in traditional and modern power grids [44]. These influencing factors also pose challenges to the development of accurate models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Review Type** & **Remarks** \\ \hline
[37] & 2015 & Systematic &
\begin{tabular}{c} Reviewed and evaluated AI-based LF models, highlighting the importance of factors such as ANN architecture, input combinations, activation functions, training procedures, and exogenous variables on accuracy. \\ \end{tabular} \\ \hline
[38] & 2016 & Tutorial & Reviewed probabilistic LF, including research directions and LF strategies. \\ \hline
[39] & 2017 & Systematic &
\begin{tabular}{c} Organized LF techniques with a focus on scenario-specific model selection. Provided a taxonomy for model selection based on challenges. \\ \end{tabular} \\ \hline
[40] & 2017 & Comparative & Compared STLF using different RNN architectures. \\ \hline
[41] & 2020 & Comprehensive & Examined STLF using single, hybrid, and combined approaches. \\ \hline
[42] & 2021 & Narrative &
\begin{tabular}{c} An analysis of the residential, commercial, industrial, and off-grid sectors was reported. It highlighted the limitations of current LF models and proposed solutions. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: Methodology of the paper. Forecasting involves input variables (past and present data), forecasting methods (analyzing trends), and output variables (future predictions). Electric load forecasting is categorized into four groups according to the distribution of time stamps (Figure 5): LTLF, MTLF, STLF, and VSTLF [53]. Different techniques, including expert systems, statistical methods, AI (e.g., ML, deep learning), and hybrid models, are used for different time domains. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Factors**} & \multicolumn{5}{c}{**Affects**} \\ \hline Time & Influences load patterns, requires historical data analysis [45]. \\ \hline Meteorology & Temperature, humidity, wind, etc. influence load consumption [46]. \\ \hline Region & Unique climates affect regional electricity demand, adaptation is crucial [47]. \\ \hline Application & Model effectiveness depends on the intended grid application [38]. \\ \hline Economics & Industrialization and consumer behavior [48]. \\ \hline Events & Pandemic and special events impact load patterns and sectors differently [49]. \\ \hline Past information & Historical data vital for training, model direction, and properties adaptation [50]. \\ \hline Data quality & Reliable data are essential for accurate LF and model performance [51]. \\ \hline Technology & Advancements bring greener power systems but also introduce uncertainty in LF [51]. \\ \hline DERs & Commercial and residential input affects LF accuracy, necessitates meter-level LF [52]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influential Factors Affecting Load Curve.