## 5 Methods for Short-Term Load Forecasting
### Intelligent-Computing-Based Models  
The intelligent-computing-based models (ICMs) are divided into machine learning and expert-system-based models. Machine learning includes 10 models, while expert-system-based models include 4 methodologies that address non-linear challenges in real-world load forecasting. Since the mid-1980s, researchers have focused on ANNs as powerful tools for load forecasting. ANNs outperform previous methods for non-linear inputs, complex relationships, adaptive control, and pattern prediction. Unlike statistical models, ANNs map input-output relationships without complicated dependencies. Their training process retrieves non-linear relationships, allowing effective load demand prediction with the right algorithm, training data, and network structure [111]. Table 9 provides an overview of 45 machine learning models and 8 expert systems, including ANN, DL, MLP, SVM, ELM, SOM, DT, RNN, LSTM, GP, FCMs, FRBS, and FR applied in ICM for STLF. Table 6 provides overviews for machine learning and expert-system-based methods that could lead to the selection of an appropriate ICM according to the constraints. The MAPE, MAE, and RMSE values presented in Table 9 are derived from the respective research papers studied and are not intended for direct comparison in this paper [43, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162]. The purpose of presenting the performance accuracy of each paper is to provide readers with a comprehensive review, enabling them to assess each model's performance based on its dataset and input parameters. \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}{*}{**Contribution**} & \multirow{2}{*}{**Benchmarked**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Accuracy** \\ **(Best-Performing Model)** \\ \end{tabular} } \\  & & & & **Against** & \\ \hline
2010 & 2004–2008 & \
\begin{tabular}{c} Combined time-series and ML models with filters, the \\ UK \\ \end{tabular} &
\begin{tabular}{c} Time series, machines \\ learning \\ \end{tabular} & – & 2.01 & 0.17 \\ \hline
2010 & 1985–1991 & \
\begin{tabular}{c} Modified the SVR algorithm using LWR in risk function \\ with Mahalanobis-based weighting. The LSWR \\ outperformed other methodologies. \\ \end{tabular} &
\begin{tabular}{c} LWR, local SVR, \\ LWSVR \\ \end{tabular} & – & – & 1.34 \\ \hline
2011 & 1974–2003 & \
\begin{tabular}{c} Designed experiments and proposed a conventional \\ regression and ANN-MLP-based flexible model. \\ \end{tabular} &
\begin{tabular}{c} ANN-MLP, regression, \\ actual data \\ \end{tabular} & – & 0.01 & 0.03 \\ \hline
2016 & 2002–2004 &
\begin{tabular}{c} Mainly suggested straightforward models for daily STLF \\ cycles using regression. \\ \end{tabular} & ARIMA, ES, MLP & – & 1.35 \\ \hline
2017 & 2014–2014 & \
\begin{tabular}{c} Proposed SVR for office building baseline forecasting, \\ enhancing real-time effectiveness. \\ \end{tabular} &
\begin{tabular}{c} Simple average model, \\ actual data \\ \end{tabular} & – & 1.57 & – \\ \hline
2018 & 2009–2012 &
\begin{tabular}{c} Incorporating non-linear trend variables and unit \\ commitment enhances forecasting integration. \\ \end{tabular} & ILP, PLAQ-RM & – & 0.81 \\ \hline
2018 & 2007 and 2013 &
\begin{tabular}{c} Developed SSVRE for precise SVR learning execution, \\ enhancing computational accuracy. \\ \end{tabular} & SVR, SSVRE & 0.11 & – & – \\ \hline
2020 & 2009–2010 & \
\begin{tabular}{c} Proposed a PAR-based model via group and distinct LF \\ learning to address smart grid complexity issues. \\ \end{tabular} &
\begin{tabular}{c} SGDR, FTRLP, OSELM, \\ PAR \\ \end{tabular} & – & 1.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._