## 7 Discussion  
### Accuracy  
Power generation and distribution infrastructure decisions rely on accurate STLF. Evaluation metrics such as RMSE, MAE, and MAPE are considered in this study. Different models use diverse variables and datasets, making direct comparison challenging, which can only be performed by comparing models that use similar influencing factors and datasets. This study presents precision results (Tables 8-10), taking into account the specific objectives of each model for clarity. This study found that ICM approaches outperformed statistical methods. Combining statistical methods with RM, metaheuristic models, ES, and ANN models improved accuracy. Hybrid approaches generally outperformed ICM \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
2022 & 2020–2021 &
\begin{tabular}{c} A CNN-Bi-GRU hybrid model was developed for short-term 3-phase LF in smart solar microgrids, outperforming traditional models. \\ \end{tabular} & RNN, LSTM, GRU, Bi-LSTM, Bi-GRU & – & 0.03 & – \\ \hline
2022 & 2013 &
\begin{tabular}{c} Presented a superior STPLF methodology using DPMM, MCMC, and ensemble learning, surpassing benchmarks in uncertainty modeling. \\ \end{tabular} & CKDE, ARIMA, QRTE, CED & – & – & – \\ \hline
2022 & 2019 &
\begin{tabular}{c} Explored the potential of electric vehicle batteries in home energy management systems, offering optimal designs and insights. \\ \end{tabular} & Actual load & – & – & – \\ \hline
2022 & 2016–2019 & \
\begin{tabular}{c} Addressed STLF for smart grids, emphasizing the significance of deep learning in achieving superior accuracy, as evidenced in Panama case study. \\ \end{tabular} &
\begin{tabular}{c} DLR, Bi-LSTM, GRU, \\ SVR, XGB, AdaBoost, \\ random forest \\ \end{tabular} & – & – & 2.9 \\ \hline
2022 & 2018 & \
\begin{tabular}{c} Developed a pooling-based DL algorithm, utilizing \\ Copula function and Hankel matrix, significantly \\ enhancing EMS forecasting accuracy. \\ \end{tabular} & SVM, DBN &
\begin{tabular}{c} 2.86 \\ 2.46 \\ \end{tabular} & – & – \\ \hline
2023 & 2022 &
\begin{tabular}{c} Introduced an IoT-based STLF model for electric power consumption and RES generation, enhancing \\ demand-side management in smart buildings. \\ \end{tabular} & Decision tree regression, random forest regression & – & – & 0.55 \\ \hline
2023 & 2022 &
\begin{tabular}{c} An innovative IoT-based smart meter with high data \\ rates was developed, featuring cloud-based storage and energy estimation, contributing to smart grid structure. \\ \end{tabular} & Actual load & – & – & – \\ \hline
2023 & 2010–2013 &
\begin{tabular}{c} Examined privacy-preserving and \\ communication-efficient federated learning for net \\ metering, combining hybrid DL predictor, IPFE, and \\ CAT for superior results. \\ \end{tabular} & CNN, LSTM, CNN-LSTM & 0.57 & 0.32 & – \\ \hline
2023 & 2009–2010 &
\begin{tabular}{c} Offered flexible probabilistic LF approach using \\ Bernstein polynomial stabilizing flow, demonstrating \\ superior performance over Gaussian and QR-based \\ methods, especially in low-data scenarios. \\ \end{tabular} & Fully connected NN, & & & & \\ \hline
2023 & 2018 &
\begin{tabular}{c} Customized pricing scheme for an electricity market, \\ enhancing the agent’s profit by 4\% to 130\% compared to \\ uniform pricing utilizing LSTM, considering BTM PV and energy storage. \\ \end{tabular} & Actual load & 17.8 & – & 1.31 \\ \hline
2023 & 2009–2010 & \
\begin{tabular}{c} Proposed a global modeling framework for LF in \\ distribution networks, outperforming local models and \\ offering scalability and efficiency. \\ \end{tabular} &
\begin{tabular}{c} MLR, LSTM, N-BEATS, \\ \(K\)-means LSTM \\ \end{tabular} & – & 0.80 & 8.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._methods.