## 5 Methods for Short-Term Load Forecasting
### Intelligent-Computing-Based 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 Model)** & \\ \hline
2016 & 2010 & Developed DCANN and UDCANN based on IBSM, & BP-ANN, FNN, & - & - & 8.28 \\
[127] & Australia & combining supervised and unsupervised learning. & LSSVM, GARCH & - & - & 1.65 \\ \hline
2016 & 2011–2011 & Proposed innovative STLF combining BP-ANN, ANFIS, & BP, ANFIS, D-SARIMA for varied data. & - & - & 1.65 \\ \hline
2016 & 2010–2011 & A neural network for LV/MV networks boosts accuracy with varied models for intra-day and daily average power change. & Time-series models, naive model & - & 3.63 & 10.3 \\ \hline
2016 & 2011–2014 & Novel RELM combines ELM and RNN for enhanced electricity LF. & ELM, LR, GRNN & 0.03 & – & – \\ \hline
2016 & 2009 & Proposed an ensemble method using wavelet transform, & Actual load, WNN, & - & - & 2.02 \\
[131] & ISO-NE & ELM, and PLS regression for STLF. & SVR, ELM & - & - & 1.41 \\ \hline
2017 & 2010–2012 & Implemented MLP for LF; claimed NN yields better results in several sectors. & LR, SVR, DNN, CNN & - & - & 1.41 \\ \hline
2018 & 2009–2010 & Compared forecasting techniques for South Korea using 20 variables, laying the foundation. & SVR, Fuzzy-Rough, & - & - & 2.13 \\ \hline
2017 & 2013 & Proposed EMD-based ensemble DL for STLF, integrating & SVR, ANN, DBN, RF, & - & - & 0.67 \\
[134] & Australia & adaptive signal processing. & EMD-SVR & - & - & 0.67 \\ \hline
2017 & 2016 & Developed SOM for microgrid planning, identifying & Actual load & - & - & - \\
[135] & Chile & distinct family consumption patterns. & & & & \\ \hline
2018 & 2009–2010 & Proposed a novel deep RNN architecture for household & ARIMA, RNN, SVR, & 0.45 & 0.25 & – \\
[136] & Ireland & LF employing both conventional and recurrent layers. & DRNN, PDRNN & - & - & 4.53 \\ \hline
2018 & 2011–2012 & Introduced a prediction model considering the impact of & ARIMA, SVR, BRNN, & - & - & 4.53 \\
[137] & Hungary & EVs on building loads. & RBFNN & - & - & 4.53 \\ \hline
2018 & 2013–2013 & Created a strategy of decomposition–reconstruction by & & & & \\
2018 & 2013–2013 & Integration of singular spectrum analysis. A variety of forecasting models, including ARIMA, ES, and NNs, & LSSVM, CSA-SVM, & - & - & 0.59 \\
[138] & Australia, & were used in the ensemble to estimate future values of & GA-BPNN & - & - & 0.59 \\ \hline
2019 & 2016 & Proposed a novel approach combining the copula model & NN, SVR, ELM, & - & - & 2.36 \\
[139] & USA & and DNN for accurate STLF. & R-DNN, DBN & - & - & 4.59 \\ \hline
2019 & 2004–2007 & Developed the MOFTL algorithm, evaluated on & GRNN, MOWCA, & & & - & 4.59 \\
2019 & Texas and ISO-NE, & & with ANN (MOFTL-ANN) for weight optimization. & & & & \\ \hline
2019 & 1997–1998 & Combined BRNNs and DBNs for STLF to effectively & & & & \\
2019 & 1997–1998 & Capture temporal and spatial dependencies. Input is & SVR, LSTM, BPNN & 28.5 & – & 1.95 \\
[141] & China, & processed by BRNN forwards/backwards, then fed to & & & & \\  & EUNITE & DBN for high-level representation learning. & & & & \\ \hline
2019 & 2009–2010 & Suggested an LSTM network with pinball loss for & & QRNN, QGBRT & 2.18 & – & – \\
[142] & Ireland & probabilistic forecasting on large consumer dataset. & & & & \\ \hline
2019 & 2012–2016 & Developed AI and DL-based LF system, examined & & & & \\
[143] & SA & OP-ELM and LSTM models considering weather impact. & ANFIS, OP-ELM & 0.06 & 0.05 & 0.13 \\ \hline
2019 & 2014–2018 & Proposed multi-scale CNN with time cognition for & & DM-MS-CNN, & & & \\
[144] & Ireland & enhanced multi-step STLF. & DM-GCNN & - & - & 3.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._