## 5 Methods for Short-Term Load Forecasting
### Statistical Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Contribution**} & \multirow{2}{*}{**Benchmarked**} & \multicolumn{2}{c}{**Accuracy**} \\  & & & & **(Best-Performing Model)** & \\  & **Duration** & & **Against** & & \\ \hline
2012 & 2006–2007 & ARMAX-GARCH models advance in long-horizon & ARMAX-GARCH & – & 2.63 & – \\
[88] & USA & forecasting for MISO, USA. & ARMAX-GARCHY, & & 1.79 & – \\ \hline
2018 & 2011 & Developed hourly ARMAX for EDF and ILF, performed & ARMAX-GARCHY, & & 1.79 & – \\
[89] & Netherlands & exceptionally in risk-aware activity decisions. & ILS, CAViaR, CARE & & \\ \hline BVAR & & & & & \\ \hline
2008 & 2007 & Utilized the Bayes method for input group selection to & BP-NN, Bayesian-SVR & – & 1.61 \\
[90] & China & improve forecasting performance. & BP-NN, Bayesian-SVR & – & 1.61 \\ \hline GARCH & & & & & \\ \hline
2010 & 2002 and & Combined ARMA and GARCH with wavelet transform & ARIMA, & & \\
[91] & 2006 & for enhanced historical series. & ARIMA-GARCH, CNN & – & 1.16 \\
[92] & Spain, USA & Proposed GARCH for deregulated electricity prices, & ARIMA, actual data & – & 9 & – \\
[92] & Spain, USA & outperforming ARIMA, enhanced by demand. & ARIMA, actual data & – & 9 & – \\ \hline
2009 & 2000–2002 & Proposed \(k\)-factor GIGARCH, predicting long-memory & SARIMA, GARCH & 3.63 & – & – \\
[93] & Germany & seasonality in electricity prices. & SARIMA, GARCH & 3.63 & – & – \\ \hline SEGARCH & & & & & \\ \hline
2009 & 1993–2007 & Incorporated GARCH variances and autoregressive & WARCH-ANN, & & & \\
[94] & Taiwan & errors. & SEGARCH-ANN & – & 2.56 \\ \hline EGARCH & & & & & \\ \hline
2008 & 2007–2007 & Examined ARIMA and its EGARCH variant models’ & ARIMA-EGARCH, & & & \\
[95] & USA & in-sample and out-of-sample forecasting performance. & ARIMA-EGARCH-M & – & 10.5 & – \\ \hline
2019 & 2009–2016 & Proposed EGARCH model for accurate electricity price & ARMA and extensions & – & – & 8.87 \\
[96] & Spain & regional in the Australian and Spanish markets. & ARMA and extensions & – & & \\ \hline ES & & & & & \\ \hline
1971 & 1966–1967 & Implemented general ES for STLF, which offered & Actual data & – & 2 & – \\
[97] & USA & operational simplicity and high accuracy. & & & \\ \hline
2010 & 2001–2006 & Extended double seasonality methods capturing & SES, DES, TES, & & \\
[98] & UK, France & intra-year, intra-week, and intra-day cycles. & actual data & – & 1.7 \\ \hline
2012 & 2007–2009 & Explored ES methods for STLF, introduced SVD-ES, & ES methods, ARMA, & & & \\
[99] & UK, France & enhanced efficiency for intra-day. & ANN, actual data & – & 2 \\ \hline
2018 & 2011 & Created SES-GP model, using SES smoothing for GP & GP model, actual data & – & 1.26 & – \\
[100] & China & sequence development. & & & \\ \hline RM & & & & & \\ \hline
1990 & 1982–1985 & Suggested novel LR model for holiday load forecasting & Actual data & – & 0.44 \\
[101] & USA & using binary variables. & & & \\ \hline
1998 & 1997 & Introduced NPR-based method, forecasting directly & NPR, NPR-Static, ANN & – & 2.64 & 3.57 \\
[102] & USA & from historical data with ANN outperformance. & & & \\ \hline
2008 & 1999–2003 & Developed hybrid NN model using novel data & Hybrid LR, proposed & – & – 1.5 \\
[103] & Iran & clustering, surpassing hybrid LR. & model & & \\ \hline
2009 & 1970–2007 & Forecasted Italy’s consumption using various regression & Regression models, & & \\
[104] & Italy & models with stationary data. & national forecasts & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._