## 6 Conclusions  
This study proposes a hybrid model based on the SD selection, CEEMDAN signal processing technique, and the BiLSTM network. The proposed approach was compared with LSTM, GRU, SD-BiLSTM, and CEEMDAN-BiLSTM models to assess the effectiveness of the proposed model on hourly EV charging load forecasting. To achieve the best performance, fine-tuning and proper hyper-parameters were investigated. The performance of these prediction models was evaluated in terms of MAE, RMSE, and MAPE. The main conclusions of this study can be summarized as follows:  
1. The hybrid approach is feasible and reasonable for the three-day-ahead load forecasting of a real-life dataset that was collected from 1000 EVs in nine provinces in Canada from 2017 to 2019. 2. The SD algorithm applied for optimizing the single models and the CEEMDAN technique applied in extracting the various components could both improve the prediction performance of the single models. 3. Overall, the proposed SD-CEEMDAN-BiLSTM model shows a competitive technique for enhancing the charging load prediction accuracy of the EV fleet. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GRU** & **LSTM** & **Bi-LSTM** & \begin{tabular}{c} **SD-** \\ **BiLSTM** \\ \end{tabular} & \
\begin{tabular}{c} **CEEMDAN-** \\ **BiLSTM** \\ \end{tabular} &
\begin{tabular}{c} **SD-CEEMDAN-** \\ **BiLSTM** \\ \end{tabular} \\ \hline January & 7.27 & 7.29 & 6.41 & 4.46 & 3.72 & 2.23 \\ February & 7.10 & 9.01 & 5.77 & 3.84 & 4.00 & 2.14 \\ March & 6.70 & 4.93 & 4.60 & 4.72 & 3.95 & 2.88 \\ April & 8.64 & 5.38 & 5.61 & 3.18 & 3.16 & 2.57 \\ May & 9.82 & 6.73 & 5.08 & 3.20 & 3.70 & 2.66 \\ \hline Avg. & 7.90 & 6.67 & 5.49 & 3.88 & 3.70 & 2.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MAPE (%) of all the models per month for the three-day-ahead prediction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GRU** & **LSTM** & **Bi-LSTM** & \begin{tabular}{c} **SD-** \\ **BiLSTM** \\ \end{tabular} & \
\begin{tabular}{c} **CEEMDAN-** \\ **BiLSTM** \\ \end{tabular} &
\begin{tabular}{c} **SD-CEEMDAN-** \\ **BiLSTM** \\ \end{tabular} \\ \hline January & 5.68 & 6.45 & 5.32 & 3.63 & 3.18 & 2.21 \\ February & 10.13 & 7.84 & 6.16 & 3.78 & 3.01 & 2.00 \\ March & 4.66 & 5.58 & 5.82 & 3.73 & 3.10 & 1.76 \\ April & 7.48 & 6.51 & 5.99 & 4.10 & 3.13 & 2.76 \\ May & 7.25 & 6.94 & 4.83 & 3.57 & 2.71 & 2.54 \\ \hline Avg. & 7.04 & 6.66 & 5.62 & 3.76 & 3.02 & 2.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MAPE (%) of all the models per month for the one-day-ahead prediction. **Author Contributions:** Conceptualization, A.M.; methodology, A.M.; software, A.M. and F.B. ; formal analysis, A.M., E.E. and F.B. ; investigation, A.M. and E.E. ; writing--original draft preparation, A.M., E.E. and F.B. ; writing--review and editing, A.M., E.E. and F.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is financially supported by Natural Resources Canada, the Program of Energy Research and Development under grant CEO-19-112. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within this review article. **Acknowledgments:** The authors would like to thank the Office of Energy Research and Development (OERD) of Natural Resources Canada for their valuable financial support. **Conflicts of Interest:** The authors declare no conflict of interest.