## 4 Results  
Six predictive combination models including the SD selection, the CEEMDAN method, and three different RNN architectures (GRU, LSTM, BiLSTM) were selected for comparative  
Figure 6: The original data sequence of the aggregated daily charging load (**a**) and the result of CEEMDAN; daily (**b**) weekly (**c**), monthly (**d**), seasonally (**e**). analysis and for verifying the predictive performance of the proposed model. The six predictive combination models in this research are implemented using Python Anaconda. Figures 7 and 8 illustrate the results of the aggregated charge power, in kilowatts (kW), during the three-day-ahead prediction period for all single and hybrid prediction models in spring and winter, respectively. The training period is from 20 August 2017 to 31 December 2018 and the three-day-ahead prediction period is from 30 April 2019 to 3 May 2019 and from 26 January 2019 to 29 January 2019 for spring and winter, respectively. It can be seen that the peak charging load during the three-day-ahead period occurred around 7 p.m., and the real error, which is the difference between actual and predicted load, is the largest at that point (Figures 7 and 8). However, the proposed hybrid model that includes the SD selection and decomposition method outperformed the single (Figures 7b and 8b) and other hybrid models (Figures 7c and 8c) at the peak points. Although all six models show good predictive results on the three-day-ahead period, the SD-CEEMDAN-BiLSTM model achieves a comparatively better performance with the smallest forecasting MAPE of 2.63% Canada-wide. Such trends in terms of improvements of the models using hybrid methods for electricity load, wind speed, solar radiation, and crude oil price prediction performance have also been observed in reported experimental studies [5; 6; 7; 12]. The aggregated charge power (kW) in spring and winter are compared and the results show that the peak periods in spring are shorter than in winter, resulting in higher overall charge power in winter (Figures 7 and 8). This might be due to decreased battery efficiency, a higher charging load for cabin heating, and more charging load per season in cold winter weather. Comparisons between the forecasting curve of the single models in spring and winter (Figures 7b and 8b) show that the BiLSTM curves follow the actual data better than other models. Comparisons between the prediction curves of the hybrid models in spring and winter (Figures 7c and 8c) show that the predicting curve of the proposed SD-CEEMDAN-BiLSTM model is closer to the original charge power curve than the other models. Table 2 shows the error values of the six prediction models. The training period is from 20 August 2017 to 31 December 2018, and the prediction period is from 1 January 2019 to 30 May 2019. Among the six prediction models, the results obtained by using SD-CEEMDAN-BiLSTM are more fitted to the original data. The RMSE, MAPE, and MAE values are 14.5, 2.6, and 13.1, respectively, which are much smaller than other prediction models. These data indicate that the SD-CEEMDAN-BiLSTM model has better stability and accuracy and can be well applied to EV load prediction. LSTM has the maximum MAPE value. Although all three single models and other hybrid models followed the general trend of the raw data, their forecasting errors were high. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\  & **MAE** & **MAE** & **MAPE** & **MAPE** & **RMSE** & **RMSE** \\ \hline GRU & 23.57 & 22.50 & 6.26 & 6.42 & 30.47 & 30.34 \\ LSTM & 25.70 & 25.14 & 6.44 & 7.26 & 28.04 & 33.70 \\ Bi-LSTM & 20.09 & 22.37 & 5.03 & 6.43 & 27.80 & 29.81 \\ SD-BiLSTM & 18.46 & 18.93 & 3.98 & 4.33 & 21.59 & 23.77 \\ CEEMDAN-BiLSTM & 15.23 & 16.90 & 3.42 & 3.81 & 18.01 & 18.24 \\ SD-CEEMDAN-BiLSTM & 11.02 & 13.10 & 2.54 & 2.63 & 13.64 & 14.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction evaluation indicators for six models.