## 6 Smart Grid
\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 Model)** & \\ \hline
2020 & 2013 & \
\begin{tabular}{c} Efficient hierarchical clustering using dissimilarity \\ measures captures diverse household consumption \\ patterns in smart meter data. \\ \end{tabular} &
\begin{tabular}{c} ACORN-Grouped \\ \end{tabular} & – & – & – \\ \hline
2020 & 2013 & \
\begin{tabular}{c} Proposed a CNN-LSTM hybrid for individual household \\ STLF, outperforming state-of-the-art models. \\ \end{tabular} &
\begin{tabular}{c} LSTM, BPNN, KNN, \\ ELM \\ \end{tabular} & – & – & 40.3 \\ \hline
2020 & 2017 & \
\begin{tabular}{c} Developed a hybrid DL scheduling algorithm for smart \\ SK \\ \end{tabular} &
\begin{tabular}{c} Ring buffer, class-means \\ _K_-means, CosSim, \\ __i_r_-incremental DL \\ \end{tabular} & 0.09 & – & – \\ \hline
2020 & 2017 & \
\begin{tabular}{c} Introduced ST-BTMLPVF problem, developed ST-GAE \\ and STGDL optimization for accurate BTM load and PV \\ forecasting. \\ \end{tabular} &
\begin{tabular}{c} SVR, GRU, LSTM, \\ DRNN \\ \end{tabular} & 0.19 & 0.13 & 8.01 \\ \hline
2020 & 2012-2016 & \
\begin{tabular}{c} Offered a DRNN Bi-LSTM model for accurate STLF, \\ Czech \\ Republic \\ \end{tabular} &
\begin{tabular}{c} RNN, FFNN, \\ Copula-DBN, \\ CNN-RNN, D-CNN \\ \end{tabular} & – & – & 1.18 \\ \hline
2020 & 2016-2018 & \
\begin{tabular}{c} Combining best-basis stationary wavelet packet \\ transform and Harris-hawk-optimization-based \\ feed-forward NN model for STLF in microgrids, \\ significantly outperforming existing models. \\ \end{tabular} &
\begin{tabular}{c} PSO-ANN, \\ PSO-LSSVM, BPNN \\ \end{tabular} & – & – & 1.27 \\ \hline
2021 & 2012 & \
\begin{tabular}{c} Optimized accuracy in aggregated baseline load \\ estimation under high behind-the-meter PV penetration \\ using a 2-stage decoupled approach. \\ \end{tabular} &
\begin{tabular}{c} Low5 of10, High5 of10, \\ Mid4 of6, MLR, SVR \\ \end{tabular} & – & – & 26.5 \\ \hline
2021 & 2016-2018 & \
\begin{tabular}{c} Proposed bottom-up method for HV load forecasting, \\ achieving superior accuracy and narrow intervals, \\ aiding grid management. \\ \end{tabular} &
\begin{tabular}{c} Bottom-up, ES method, \\ BELM \\ \end{tabular} & 0.32 & 2.47 & 2.48 \\ \hline
2021 & 2017-2019 & \
\begin{tabular}{c} Proposed parallel processing for fast, accurate STLF, \\ Spain \\ \end{tabular} &
\begin{tabular}{c} DT, LR, NN, SVR, RF, \\ GBRT \\ \end{tabular} & – & – & 10.9 \\ \hline
2021 & 2012 & \
\begin{tabular}{c} Examined graph ANN for spatial-temporal STLF, \\ achieving significant gains over traditional methods, and \\ proposed self-adaptive models. \\ \end{tabular} &
\begin{tabular}{c} FC-LSTM, CNN-GRU \\ \end{tabular} & – & 0.97 & 4.53 \\ \hline
2021 & 2010-2014 & \
\begin{tabular}{c} Introduced a novel multi-task multi-information fusion \\ DL framework, outperforming state-of-the-art methods \\ in forecasting accuracy. \\ \end{tabular} &
\begin{tabular}{c} SVR, XGBoost, \\ pooling-based deep \\ RNN, CNN-LSTM, \\ M-LSTM \\ \end{tabular} & – & 0.59 & 6.36 \\ \hline
2021 & 2010-2014 & \
\begin{tabular}{c} A CNN-LSTM time-series LF model for V2G services \\ was developed, demonstrating improved predictive \\ capability over regression models and adaptability to \\ market events, crucial for service reliability. \\ \end{tabular} &
\begin{tabular}{c} CNN-LSTM, actual \\ load \\ \end{tabular} & – & 21.9 & – \\ \hline
2022 & 2011-2014 & \
\begin{tabular}{c} A hybrid scheme balancing perturbation and \\ cryptography ensures efficient and privacy-preserving \\ smart meter data publishing. \\ \end{tabular} &
\begin{tabular}{c} DDF, LDF, raw data \\ \end{tabular} & – & – & 3.3 \\ \hline
2022 & 2019 & \
\begin{tabular}{c} Efficient reduced model leverages hierarchical \\ dimension reduction for scalable and accurate STLF \\ using fine-resolution smart meter big data. \\ \end{tabular} &
\begin{tabular}{c} Cluster-based approach, \\ aggregated model, \\ reduced model \\ \end{tabular} & 0.8 & – & – \\ \hline
2022 & 2013 & \
\begin{tabular}{c} FL using LSTM and FL + HC optimize residential load \\ forecasting with improved accuracy and computational \\ efficiency. \\ \end{tabular} &
\begin{tabular}{c} FL, FL + HC, \\ FL + HC \(\rightarrow\) LFT \\ \end{tabular} & 0.02 & – & – \\ \hline \hline \end{tabular}
\end{table}