## 5 Methods for Short-Term Load Forecasting
### Statistical Methods
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Data**} & \multicolumn{2}{c}{**Benchmarked**} & \multicolumn{2}{c}{**Accuracy**} \\  & \multicolumn{2}{c}{**Duration**} & \multicolumn{2}{c}{**Contribution**} & \multicolumn{2}{c}{**Against**} & \multicolumn{2}{c}{**Model**} \\ \cline{3-6}  & & & & & & \\ \hline ARMA & & & & & & \\ \hline
1987 & 1983–1984 & Enhanced ARMA with polynomial regression for a & ARMA, TFM, & & & \\
72 & USA & non-linear extension of TFM for optimal forecasting. & non-linear, & & 3.7 & – \\ \hline
1994 & \textgreater{}4 Weeks & Applied ARMA with WRLS algorithm for online model & Actual load & – & \textless{}2.5 & – \\
73 & USA & parameter estimation in power distribution. & Actual load & – & 1.3 & – \\ \hline
2002 & 1998–2000 & ARMA fitted after analyzing deseasonalized data; & Actual load & – & 1.3 & – \\
74 & USA & hyperbolic distribution offers an excellent fit. & Actual load & – & 1.6 & – \\ \hline
2006 & 2001–2002 & Combined offline learning with online forecasting while adjusting weights to conditions. & ARMA, hybrid & – & 1.6 & – \\ \hline
2013 & 2008–2010 & ARMA-GARCH models and performance evaluation of their modifications for STLF. & ARMA-GARCH(-M) & & 7.4 & 0.2 & 14.5 \\ \hline ARIMA & & & & & \\ \hline
2000 & 1994–1995 & Introduced a non-linear statistical dependence measure; & ARIMA, ARMAX, & – & 0.8 \\
76 & CR & compared ANN and non-linear models. & ANN & – & 1.98 \\ \hline
2001 & 1991–1997 & Proposed modified ARIMA with operator estimation; & ARIMA, modified & – & 1.98 \\
77 & Iran & outperformed ARIMA and ANN. & ARIMA, ANN & & 3.8 & 2.7 & – \\ \hline
2018 & 2012–2015 & MARS yielded most accurate results at 0.5 h and 1.0 h & ARIMA, MARS, SVR & & & \\
78 & Australia & horizon, while SVR performed well at 24 h horizon. & ARIMA, MARS, SVR & & & \\ \hline
2020 & 2017–2018 & Suggested model integrates auto-ARIMA and _K_-means & ARIMA, hybrid & & & \\
79 & Japan & for peak load reduction. & ARIMA & – & & 5.1 \\ \hline SARIMA & & & & & \\ \hline
2011 & 2004–2009 & Proposed MA-C-WH model excels against SARIMA in & SARIMA, actual load & – & – & 2.88 \\
80 & China & seasonal adjustments and trends. & SARIMA, F-SARIMA, & & & & \\ \hline
2012 & 2007–2010 & Fourier-based residual series enhance SARIMA precision & SARIMA, F-SARIMA, & & & & \\
81 & China & optimized by PSO. FS-SARIMA excels. & S- SARIMA, FS-SARIMA & & & & \\ \hline
2013 & 2009–2009 & SARIMA-SVM model outperformed SARIMA and SVM & SARIMA, SVM, & & & & \\
82 & Italy & in PV power forecasting. & actual data & & & & \\ \hline
2018 & 2012–2015 & Developed interactions-ARIMAX method; enhancing predictive performance and accuracy. & SARIMA, MLR & – & – & 0.7 \\ \hline
2019 & 2007–2008 & Proposed KP-SVR via SARIMA with feature selection for & ARIMA, SARIMA, & & & & \\
84 & UK, France & time series. & ANN, SVR, KP-SVR & & & & \\ \hline ARMAX & & & & & & \\ \hline
2009 & 1998–2005 & Included exogenous variables in the models, and TSK & ARMAX, NN, TSK & & & & \\
85 & Colombia & models perform well. & ARMAX, NN, TSK & & & & \\ \hline
2008 & 2005–2005 & HSPO-ARMAX overcomes local optimal points via & PSO-ARMAX, & & & & \\
86 & China & genetic algorithm crossover operation. & HPSO-ARMAX & & – & & 1.06 \\ \hline
2011 & 1995–2007 & Explored diverse population growth effects on electric & ARMAX, GARCH & – & – & & \\
87 & Spain & roads for tourism planning. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Review Based on Statistical Analysis Methodologies for STLF. (Year: Research paper published, Data Duration: Duration of the dataset used in the research paper, Contribution: Each paper’s contribution, Benchmark Against: Studied research papers compared their proposed models with different models and load, Accuracy: Proposed model accuracy presented in research papers).