## 2 Review of Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Hybrid Model** &
\begin{tabular}{c} **Main** \\ **Contributions** \\ \end{tabular} & **Findings** & **Error Value** & **Application** \\ \hline
**2024** & \begin{tabular}{c} Fuzzy support vector \\ \end{tabular} & \begin{tabular}{c} Utilized hybrid \\ \end{tabular} & \begin{tabular}{c} The FSVM enhances the \\ model’s ability to fit the \\ FSVM-GM for \\ LTLF of the power \\ system \\ \end{tabular} & \
\begin{tabular}{c} MSE = 0.6 \\ unseen data, leading to \\ improved accuracy and \\ reliability for LTLF \\ \end{tabular} &
\begin{tabular}{c} Power \\ system bus \\ \end{tabular} \\ \hline
**2024** & \begin{tabular}{c} Improved \\ variational mode \\ decomposition\(-\)whale \\ optimization algorithm \\ (IVMD-WOA- \\ LSSVM) [39] \\ \end{tabular} & \begin{tabular}{c} The hybrid of \\ IVMD-WOA- \\ proposed \\ performance \\ \end{tabular} & \
\begin{tabular}{c} The WOA algorithm \\ exhibits faster \\ convergence and has \\ great local optima \\ avoidance, leading to \\ improved optimization \\ performance \\ \end{tabular} &
\begin{tabular}{c} MAPE = 3.21\% \\ CVRMSE = 0.0344 \\ R2 = 0.9901 \\ \end{tabular} & Cooling load \\
**2024** & \begin{tabular}{c} Improved \\ variational mode \\ decomposition\(-\)whale \\ optimization algorithm \\ (IVMD-WOA- \\ LSSVM) [39] \\ \end{tabular} & \begin{tabular}{c} A hybrid of \\ GEP-ANFIS model \\ for LTLF is \\ proposed and \\ compared with \\ single GEP and \\ ANFIS \\ \end{tabular} & \begin{tabular}{c} The proposed hybrid \\ model consistently \\ achieves lower error \\ values and higher R2 \\ compared with \\ across all years \\ \end{tabular} & \
\begin{tabular}{c} RMSE = 0.0007 \\ MSE = 5.2296 \(\times\) \\ 10\({}^{-7}\) \\ \end{tabular} &
\begin{tabular}{c} Food \\ industry \\ \end{tabular} \\ \hline
**2024** & \begin{tabular}{c} Convolutional neural \\ network\(-\)long \\ short-term memory \\ (CNN-LSTM) [41] \\ \end{tabular} & \begin{tabular}{c} The hybrid of \\ CNN-LSTM is \\ proposed and \\ integrates with \\ feature selection \\ (FNE) and data \\ decomposition \\ technique (EMD) \\ \end{tabular} & \
\begin{tabular}{c} The EMD and FE \\ techniques substantially \\ enhance forecasting \\ accuracy, leading to \\ significant \\ improvements in both \\ model performance and \\ computational \\ complexity \\ \end{tabular} &
\begin{tabular}{c} Residential \\ RMSE = 102.95 \\ \end{tabular} \\ \hline
**2023** & \begin{tabular}{c} Sequential pattern \\ mining\(-\)long short-term memory \\ memory \\ (SPM-LSTM) [42] \\ \end{tabular} & \begin{tabular}{c} Hybrid of \\ SPM-LSTM is \\ proposed for STLF \\ that uses load and \\ meteorological \\ data \\ \end{tabular} & \begin{tabular}{c} SPM-LSTM \\ outperforms LSTM, \\ LSTM-ANN, and \\ CNN-GA in terms of \\ accuracy, while also \\ requiring less training \\ and response time \\ \end{tabular} & \
\begin{tabular}{c} RMSE = 387.06 \\ MAE = 207.63 \\ CV-RMSE = 8.45\% \\ R2 = 0.951 \\ \end{tabular} &
\begin{tabular}{c} Cities in Spain \\ \end{tabular} \\ \hline
**2022** & \begin{tabular}{c} Supervised \\ training\(-\)the \\ training \\ \end{tabular} & \begin{tabular}{c} Introduced a \\ feature extraction \\ method that \\ combines \\ frequency \\ component \\ analysis (FCA) \\ with firefly \\ algorithm (FA) \\ optimization for \\ data compression \\ \end{tabular} & \
\begin{tabular}{c} Introduced a \\ outperforms LSTM, \\ LSTM-ANN, and \\ CNN-GA in terms of \\ accuracy, while also \\ R2 = 0.951 \\ R2 = 0.951 \\ \end{tabular} &
\begin{tabular}{c} \\ \multirow{2}{*}{Residential} \\ \multirow{2}{*}{MAE = 2.40\%} \\ \end{tabular} \\ \cline{3-3}
**2022** & \begin{tabular}{c} Fuzzy cluster\(-\)fireworks \\ algorithm\(-\)LSSVM \\ (FC-FWA-LSSVM) [43] \\ \end{tabular} & \begin{tabular}{c} Introduced a \\ feature extraction \\ method that \\ combines \\ frequency \\ component \\ analysis (FCA) \\ with firefly \\ algorithm (FA) \\ optimization for \\ data compression \\ \end{tabular} & \
\begin{tabular}{c} It provides high \\ accuracy forecasting \\ which minimizes RMSE \\ to 2.32\%, MAPE to \\ MAE = 2.40\% \\ \end{tabular} &
\begin{tabular}{c} RMSE = 2.19\% \\ \multirow{2}{*}{Residential} \\ \multirow{2}{*}{MAE = 2.40\%} \\ \end{tabular} \\ \cline{3-3}
**2022** & \begin{tabular}{c} Improved \\ algorithm\(-\)support \\ vector machine \\ (ISSA-SVM) [44] \\ \end{tabular} & \begin{tabular}{c} An ISSA is \\ proposed to \\ address the issues \\ with hyper \\ parameter \\ selection of the \\ SVM model for \\ mid-long term \\ load forecasting \\ \end{tabular} & \begin{tabular}{c} The ISSA-SVM can \\ effectively improve the \\ forecasting accuracy \\ compared with the \\ original SVM, BPNN, \\ MLR, ELM, and Elman \\ \end{tabular} & \
\begin{tabular}{c} MAPE = 2.18\% \\ 3.94\% \\ \end{tabular} &
\begin{tabular}{c} China’s \\ electricity \\ consumption \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}