## 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
2015 & 2011–2013 & Improved solar power forecasting using VAR framework & VAR, VARX & 8.5 & – & – \\
2016 & 2009–2010 & Boosted additive quantile regression increases & Unconditional & & & \\
2016 & Ireland & Boosted additive quantile regression increases & quantiles, & & & \\  & & probabilistic LF, particularly advantageous for & conditional quantiles, & – & – & – \\  & & disaggregated demand predictions. & additive models & & & \\ \hline
2016 & 2014 & Examined a big-data-driven EV charging demand LF model, integrating real-world traffic data, aiding power system planning and infrastructure decisions. & Actual data & – & – & – \\ \hline
2017 & 2011–2013 & Sparse coding increases individual household LF accuracy by 10\% over classical methods. & ARIMA, Holt–Winters, group sparse & – & – & 20.6 \\ \hline
2017 & 2008 & See grid offers a 2-level energy prediction framework utilizing smart meter data, minimizing privacy–utility trade-offs for smart grids. & SARMA, SVM, actual data & – & – & – \\ \hline
2018 & 2009–2010 &
\begin{tabular}{c} Novel ensemble method utilizing hierarchical clustering for improved aggregated LF with fine-grained subprofiles. \\ \end{tabular} & Actual data, individual forecast & – & – & 5.08 \\ \hline
2018 & - & LSTM-based deep learning framework advances & & LSTM, FNN, KNN & – & – & 4.24 \\
2018 & Canada &
\begin{tabular}{c} Introduced CQRA for probabilistic LF, achieving superior ensemble performance and effectively \\ \end{tabular} & BI, NS, MED, SA, WA, QRA variants & – & – & – \\
2018 & 2009–2010 & ISO-NE &
\begin{tabular}{c} Developed a novel pooling-based deep RNN for household load forecasting, overcoming overfitting \\ \end{tabular} & ARIMA, SVM, deep & & & \\ \hline
2018 & 2012–2014 &
\begin{tabular}{c} Offered sliding-window-based ARIMA algorithms integrating non-seasonal and seasonal time-series models for accurate STLF using smart meter data. \\ \end{tabular} & Four sliding-window-based ARIMA models & – & – & 9.05 \\ \hline
2019 & 2010–2013 &
\begin{tabular}{c} Pioneered Bayesian deep-learning-based LSTM for superior probabilistic net LF, leveraging subprofile clustering and PV visibility. \\ \end{tabular} & MLR, QR, QRF, LSTM & 17.2 & 13.9 & 0.09 \\ \hline
2019 & 2015–2016 & \begin{tabular}{c} A parallel gradient boosting decision tree algorithm for EV schedulable capacity forecasting was developed, \\ \end{tabular} & \
\begin{tabular}{c} SCC-PGBDT, SCC-PRF, \\ SCC-PKNN, \\ \end{tabular} &
\begin{tabular}{c} \\ SCC-PGBDT, SCC-PRF, \\ \end{tabular} & 3.99 & – & 3.97 \\
2019 & China &
\begin{tabular}{c} A novel clustering-based approach for efficient LF and \\ demand-side management using smart meter data \\ demonstrated improved accuracy and reduced \\ computational burden. \\ \end{tabular} & MLR, ANN & – & – & 4.64 \\ \hline
2020 & 2009–2010 & Multi-task Bayesian NN improves probabilistic LF, offering superior accuracy and addressing over-fitting challenges in deep learning for smart grids. & SVR, GBRT, RF, PLSTM & 0.74 & 0.44 & – \\ \hline
2020 & 2010 & \
\begin{tabular}{c} Machine-learning-based LF with expert aggregation for \\ improved accuracy by exploiting smart meter data in \\ smart grid management. \\ \end{tabular} &
\begin{tabular}{c} SSWA, MSWA, \\ 2S-MSWA \\ \end{tabular} & 21.1 & 14.2 & 5.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._