## 6 Smart Grid
\begin{table}
\begin{tabular}{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** \\ **Again** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Benchmarked** \\ **Against** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Accuracy** \\ **(Best-Performing** \\ **Model)** \\ \end{tabular} } \\ \hline
2021 & 2006, 2018, 2002, 2009 & \begin{tabular}{c} Proposed method includes wavelet transform, feature selection, and LSTM networks to enhance load and price prediction accuracy. \\ \end{tabular} & \begin{tabular}{c} Multiple hybrid \\ models \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 0.93 \\ 4.38 \\ 2.2 \\ 2.67 \\ \end{tabular} \\ \hline
2021 & 2015-2016 & \begin{tabular}{c} Combined improved FCMs, RF, and DNNs, addressing historical load data disparity for better load profiling. \\ \end{tabular} & \begin{tabular}{c} Actual load, multiple \\ hybrid models \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 0.03 \\ 0.08 \\ \end{tabular} \\ \hline
2021 & 2017, 2018-2021 & \begin{tabular}{c} Hybrid NN constituting CNN and bidirectional GRU \\ (CNN-BiGRU) using multi-model fusion. \\ \end{tabular} & \begin{tabular}{c} CNN-NN, CNN-GRU, \\ CNN-LSTM \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 4.22 \\ 5.08 \\ \end{tabular} \\ \hline
2022 & 2012-2013 & \begin{tabular}{c} Model with improved TCN and DenseNet, \\ incorporating correlation analysis and self-attention. \\ \end{tabular} & \begin{tabular}{c} Multiple hybrid \\ models \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 0.91 \\ 0.87 \\ \end{tabular} \\ \hline
2022 & 2017-2021 & \begin{tabular}{c} Integrated WNN and self-adaptive momentum factor \\ (SAMF) for accurate and stable forecasting. \\ \end{tabular} & \begin{tabular}{c} Multiple hybrid \\ models \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 0.79 \\ 1.08 \\ \end{tabular} \\ \hline
2023 & 2006-2010 & \begin{tabular}{c} Based on ResNet and LSTM, a hybrid model to enhance \\ Australia \\ \end{tabular} & \begin{tabular}{c} MLR, ResNet, \\ CNN-LSTM, \\ ResNet-LSTM \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 0.76 \\ 0.61 \\ \end{tabular} \\ \hline
2023 & 2016-2016 & \begin{tabular}{c} Model consisting of SVM, BPNN, GRNN predictors, and \\ deep ensemble model. The predictors’ performance was \\ optimized using GA and the bat algorithm. \\ \end{tabular} & \begin{tabular}{c} BPNN-PSO, ELM-GA, \\ RBF \\ \end{tabular} & \
\begin{tabular}{c} 0.01 \\ \end{tabular} &
\begin{tabular}{c} - \\ - \\ \end{tabular} \\ \hline
2023 & 2015-2017 & \begin{tabular}{c} Researched feed-forward LSTM, introducing HFSM for \\ optimal feature selection. \\ \end{tabular} & \begin{tabular}{c} LSTM, CNN, FC, \\ HFSM-LSTM \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } &
\begin{tabular}{c} 2.21 \\ 2.21 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._forecasting. Their precise control and monitoring of devices provide critical data that optimize load forecasts for efficient grid management. The continuous data streams in smart grids challenge traditional machine learning models, rendering them inadequate when confronted with new and unknown parameters influencing consumption patterns [240]. Table 12 succinctly outlines the differences between traditional and adaptive learning models and provides key insights for addressing the evolving load forecasting needs in smart grids. It serves as a comprehensive summary of pertinent information gleaned from the reviewed research articles on STLF in the context of smart grids.