## 5 Methods for Short-Term Load Forecasting
### Intelligent-Computing-Based Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{\begin{tabular}{c} **Data** \\ **Duration** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Contribution** \\ **Against** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Accuracy** \\ **(Best-Performing** \\ **Model**) \\ \end{tabular} } \\  & & & & & & \\ \hline
2019 & 2013–2013 & \begin{tabular}{c} Implemented subsampling to expedite SVR modeling. \\ 2002–2002 \\ [145] \\ \end{tabular} & \
\begin{tabular}{c} Actual data, SGA-SVR, \\ surface parameters, affirming model relevance and \\ USA \\ \end{tabular} &
\begin{tabular}{c} – \\ \end{tabular} & 5.8 & – \\ \hline
2020 & 2010, 2013, 2020 & \
\begin{tabular}{c} Proposed a synthetic STLF using a MIMO model with \\ LSSVM, employing DWT to split the signal and \\ integrating GSA for optimal input selection. \\ \end{tabular} &
\begin{tabular}{c} ANN-MIMO, \\ ANN-LSSVM \\ \end{tabular} & – & – & 2.10 \\ \hline
2020 & 2015–2017 & \
\begin{tabular}{c} Introduced COSMOS, a stacking ensemble model for \\ accurate STLF predictions. \\ \end{tabular} &
\begin{tabular}{c} Shallow NN, MLR, \\ KNN, DT, DNN \\ \end{tabular} & – & – & 6.97 \\ \hline
2020 & 2013–2016 & \begin{tabular}{c} Developed an FFNN and clustering hybridization model \\ Greece \\ \end{tabular} & \
\begin{tabular}{c} RBFNN, GRNN, ENN \\ with mid-season training. \\ \end{tabular} &
\begin{tabular}{c} – \\ \end{tabular} & – & 7.00 \\ \hline
2020 & 2017–2018 & \
\begin{tabular}{c} Proposed a novel QRF model with temperature and \\ humidity factors, involving training multiple models. \\ \end{tabular} &
\begin{tabular}{c} GEMD + QRF, EMD + \\ QRF, QET \\ \end{tabular} & – & – & 1.37 \\ \hline
2020 & 2018–2019 & \
\begin{tabular}{c} Used an ensemble of HMMs trained on industrial \\ electricity data for accurate forecasting. \\ \end{tabular} &
\begin{tabular}{c} SVR-RBF kernel, CNN, \\ LSTM \\ \end{tabular} & – & – & 7.07 \\ \hline
2020 & 2003–2015 & \
\begin{tabular}{c} Suggested MCRN for STLF, leveraging \(k\)-\(d\) tree algorithm \\ ISO-NE \\ \end{tabular} &
\begin{tabular}{c} Actual load, SVM, \\ ELM \\ \end{tabular} & – & – & 4.59 \\ \hline
2020 & 2015–2016 & \
\begin{tabular}{c} Proposed DBM model, incorporating DSM and \\ China \\ \end{tabular} &
\begin{tabular}{c} ARIMA, LSSVM, DBN \\ \end{tabular} & – & – & 3.86 \\ \hline
2021 & 2020 & \
\begin{tabular}{c} Suggested an online adaptive RNN for LF, continuously \\ adapting to new patterns. \\ \end{tabular} &
\begin{tabular}{c} LSTM, regression \\ algorithms \\ \end{tabular} & – & 0.24 \\ \hline \hline \multicolumn{6}{l}{Expert Systems} \\ \hline
2005 & 1960–1990 & \
\begin{tabular}{c} The FLR model applies [PERSON]’s fuzzy arithmetic for \\ weekend holiday forecasting. \\ \end{tabular} &
\begin{tabular}{c} Actual load, NN-Fuzzy \\ interface \\ \end{tabular} & – & 3.57 & – \\ \hline
2008 & 2002–2004 & \
\begin{tabular}{c} TSK fuzzy system predicts OEM peak price, compared \\ to statistical methods. \\ \end{tabular} &
\begin{tabular}{c} ARMAX, ANN \\ \end{tabular} & 0.08 & – & – \\ \hline
2009 & 2000–2007 &
\begin{tabular}{c} A new fuzzy logic controller enhances LF accuracy and \\ processing efficiency. \\ \end{tabular} & Actual load & – & – & 2.2 \\ \hline
2012 & 2000–2002 & \
\begin{tabular}{c} Developed Interval Type-2 FLS (IT2-FLSs) to improve LF \\ accuracy by handling uncertainties in real datasets. \\ \end{tabular} &
\begin{tabular}{c} Traditional Type-1 \\ TSK-based FLS \\ \end{tabular} & 0.16 & – & – \\ \hline
2014 & 2005–2007 & \
\begin{tabular}{c} Fuzzy interaction regression for STLF outperforms other \\ fuzzy and MLR models. \\ \end{tabular} &
\begin{tabular}{c} Multiple fuzzy \\ regression, MLR \\ \end{tabular} & – & – & 3.68 \\ \hline
2015 & 2014 & \
\begin{tabular}{c} A novel weekday STLF algorithm combines WT, \\ adaptive GA, fuzzy system, and GNN. \\ \end{tabular} &
\begin{tabular}{c} ANN, GNN \\ \end{tabular} & 0.05 & – & – \\ \hline
2018 & 2010–2012 & \
\begin{tabular}{c} An adaptive fuzzy model improves day-ahead STLF \\ using recursive clustering and identification. \\ \end{tabular} &
\begin{tabular}{c} Gustafson–Kessel \\ clustering, SARIMA \\ \end{tabular} & – & – & 0.13 \\ \hline
2020 & 2009–2010 & \
\begin{tabular}{c} Fuzzy clustering and a crow optimization \(K\)-means \\ \end{tabular} &
\begin{tabular}{c} RF, LSSVM, multiple \\ hybrid models \\ \end{tabular} & – & – & 1.63 \\ \hline \hline \end{tabular}
\end{table}