## 5 Methods for Short-Term Load Forecasting
### Hybrid 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}{*}{**Contribution**} & \multirow{2}{*}{**Benchmarked**} & \multicolumn{2}{c}{**Accuracy**} \\  & & & & **(Best-Performing** \\ \hline
2015 & 2008-2013 & BNN for selected inputs through correlation analysis and \(\ell\)2-norm and DWT for improved daily LF. & ANN, SSA-SVR & – & – & 0.19 \\ \hline
2015 & 1999–2010 & Proposed semantic-aware genetic programming for STLF, outperforming existing methods in south Italy. & LR, RBFNN, SVM & – & – & – \\ \hline
2015 & 2013-2013 & New STLF method: WT removes errors, LSSVM optimized by fruit fly optimization algorithm for parameter selection. & WT-LSSVM, & & & \\ \hline
2016 & 2009–2010 & STLF with improved ELM, Levenberg–Marquardt, and partial least square regression for accuracy. & WNN, MLR, RBFNN & – & – & 0.21 \\ \hline
2016 & 2013, & Proposed hybrid algorithm integrates conditional feature selection, LSSVM, and wavelet packet signal decomposing for price/load forecasting. & ANN-MIMO, LSSVM-MIMO & – & – & 2.11 \\ \hline
2016 & 2012–2014 & Suggested adaptive two-stage model excels in daily peak LF. & HWT, BPNN, NARX & – & – & 1.64 \\ \hline
2016 & 2015–2015 & Novel time-series LF model combines SARIMA and MetaFA-LSSVR models to handle non-linear data. & SARIMA, MetaFA-LSSVR & – & 0.03 & 14.8 \\ \hline
2016 & 2013 & Hybrid approach combining data-driven techniques and physics-based models for LF energy consumption. & ANN, SVR, LSSVM, GPR, GMM & – & – & 8.16 \\ \hline
2016 & 2006–2010 & Modified ANN combined with clustering improves STLF accuracy for Greek interconnected system buses. & Actual load, FF-ANN & – & – & 3.5 \\ \hline
2016 & 2006–2008 & Model with multiple seasonality, ANNs, non-positive constraint theory, and BFGSFA improves performance. & ARIMA, random walk, WNN, BPNN & – & – & 0.85 \\ \hline
2016 & 2014–2014 & Hybrid EMD-PSO-SVR model: EMD decomposes data, SVR for individual forecasting, PSO for parameter selection. & SVR, EMD-SVR, PSO-SVER & 0.06 & 0.04 & 2.75 \\ \hline
2017 & 2012–2014 & SDA predicts hourly electricity prices, with RS-SDA outperforming other day-ahead models. & NN, MARS, SVM, SDA, RS-SDA & – & – & 2.47 \\ \hline
2017 & 2008 & A weak-ahead LF with FFNN of three layers optimized by global best PSO. & Actual load, BPNN, LMNN, PSONN & – & – & 1.41 \\ \hline
2018 & 2015–2016 & Combined WD, second-order GNN with ADF test to increase STLF accuracy. & WD-ELMAN, GNN & – & – & 2.40 \\ \hline
2018 & 2013–2015 & Proposed STLF model considers regional climate using grasshopper optimization for SVM. & GA-SVM, PSO-SVM & – & - & 1.4 \\ \hline
2018 & 2015–2015 & Article reviewed various ML techniques and emphasized the accuracy of hybrid models. & ANN, bagging ANN & – & 0.03 & 15.7 \\ \hline
2019 & 2015–2016 & Combined RF with EEMD where EEMD decomposes data, which are analyzed in time–frequency domain. & EEMD-BPSNN, EEMD-LSVM & – & – & 4.45 \\ \hline
2018 & 2011–2012 & Proposed 3-stage hybrid model utilizing wavelet and Kalman machines and Kohonen self-organizing map for load forecasting. & ARIMA, FNN, WNN & – & 17.6 & – \\ \hline
2018 & 2014 & Introduced a 2-step hybrid optimization using the grid traverse algorithm and PSO for superior SVR load LF. & ARIMA, GA-SVM, ANN & – & – & 2.53 \\ \hline
2018 & 2013–2015 & A hybrid approach using DWT, EMD, and RVFL for enhanced forecasting. & Actual load, ANN & – & – & 2.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._