## 5 Methods for Short-Term Load Forecasting
### Statistical Methods  
Statistical models are preferred for STLF, which involve extensive data analysis to predict load demand [68]. Time-series datasets contain repeated measurements of a variable at regular intervals. Understanding data patterns is critical to interpreting historical behavior and making future projections. Statistical techniques include various models for data analysis, presentation, and correlation [69]. Table 5 provides an overview of statistical methods, including time-series, exponential smoothing, and regression models, and outlines their advantages, disadvantages, and levels of complexity. Table 8 summarizes key information from reviewed research articles on models based on statistical analysis [56; 70; 71]. The MAPE, MAE, and RMSE values presented in Table 8 are drawn from the respective studied research papers and they are not meant to draw a comparison in this paper. The purpose of presenting the performance accuracy of each paper is to offer an inclusive review for the readers to identify the performance evaluation of each model in accordance with their dataset and input parameters. Figure 6 shows the distribution of the methods studied in Table 8, which outlines the RMSE, MAE, and MAPE according to the information reported in the respective papers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Advantage** & **Disadvantage** \\ \hline ANN-CT & Models complex output/input relations. & Higher number of clusters and samples required. \\ \hline ANN-NFIS & Forecasts irregular datasets. & Increased training time for complex tasks. \\ \hline ANN-AIS & Fast search capability. & Limited extrapolation capability. \\ \hline ANN-WT & Separates multiple load frequency components. & Dependence on frequency resolution. \\ \hline ANN-PSO & Affordable computation useful in load variation. & Multiple function estimates need optimal solution. \\ \hline ANN-GA & Resolves the local minima convergence issue. & GA assists ANN parameter selection complexities. \\ \hline GNN-WT-GAFL & Mitigates concerns of BP and ANN algorithms. & Manual changes required for suitable fitness. \\ \hline EMD-PSO-SVR & Load data decomposition capability to IMF components. & Challenges in handling non-stationary and non-linear data. \\ \hline EMD-GLM-GOA & Efficient for weight and threshold parametric solutions. & Unable to predict abnormal events and traditional parameters selection is complex. \\ \hline GHSA-FTS-LS- & Good computational speed utilizing a bionic algorithm with high accuracy of prediction. & Manual parameter selection. \\ \hline T-C-IEMD-DBN & T-Copula enhances peak-time forecasting accuracy with DBN. & Process repetition required due to the stochastic nature of exogenous variables. \\ \hline GA-NARX-NN & Less complexity with fast convergence by utilizing elitist GA. & GA parameter manual selection. \\ \hline FCW-EMD \& KF-BA-SVM & More prediction accuracy with improved selection and optimizing algorithm. & Incapability of learning input data temporal features. \\ \hline GA-PSO-ANFIS & Fast execution time and selects predictors that notably affect the load pattern. & Predictor selection is complex. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 6: Statistical model distribution for STLF.