# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Forecast Model** & **Error Coefficient** & **Value** \\ \hline [PERSON] et al. [4] & 2014 & Exponential Smoothing & MSPE & MSPE = 1.8187 \\ [PERSON] et al. [21] & 2014 & Multiple Regression & MAPE & MAPE = \%22.70 \\ [PERSON] et al. [6] & 2017 & SSA and VAR & MAPE & MAPE = \%29.1 \\ [PERSON] and [PERSON] [24] & 2019 & DGA-based NGBM (1,1) & MAE & MAE \textless{} 300 vehicle \\ [PERSON] et al. [30] & 2019 & GABP and ANN & RMSE & RMSE = 16.31 \\ [PERSON] et al. [22] & 2021 & Multiple Regression & MAE, MAPE, RMSE & MAPE = \%7.26 \\ [PERSON] et al. [23] & 2021 & NGMB and PSO & MAE, MAPE, RMSE & MAE = 10071.27 \\ [PERSON] [9] & 2022 & PCA and GRNN & MAPE, RMSE & Multiple R = 0.91 \\ [PERSON] et al. [28] & 2022 & Sequence to Sequence & MAE, MAPE = RMSE & MAE + 4.7119 \\ [PERSON] vd. [29] & 2022 & OFDGPM & R2, MAE, RMSE & MAPE = \%0.2045 \\ [PERSON] et al. [31] & 2022 & RVM and Gaussian kernel & MAPE, RMSE & RMSE = 5.0893 \\ [PERSON] et al. [32] & 2022 & GWO and SVR & Optimal smoothing & R2 = 0.82 \\ [PERSON] et al. [33] & 2023 & GRU, LSTM, RNN, FC, and CNN & MAPE, RMSE & Adjusted R2 = 0.82 \\  & & Nonlinear Gray Bernoulli model & & \\ [PERSON] et al. [25] & 2023 & with time-varying parameters & NRMSE and NMAE & MAPE = \%8.83 \\  & & (TVBGM) & MAPE, MAE RMSE & RMSE = 197.01 \\ [PERSON] et al. [34] & 2023 & Bi-LSTM, ISOMAP, LLE, and PCA & MAPE, RMSE, MAE, & MAE = 159.47 \\ [PERSON] et al. [35] & 2023 & BPNN, RNN, and LSTM neural & MAPE, RMSE, MAE, & MAE = 159.47 \\ [PERSON] et al. [36] & 2023 & GRA, DWT, and Bi-LSTM & MAPE, RMSE & R2 = 0.88 \\ [PERSON] and [PERSON] [37] & 2023 & Neural Networks, Bayesian linear & RMSE, MAE, R2 & R2 =0.848 \\ [PERSON] and [PERSON] [26] & 2024 & Winters Method & MAPE, R2 & MAPE = \%14.27 \\ [PERSON] et al. [27] & 2024 & ARIMA, Exponential Smoothing & MAPE & MAPE = \%44.74 \\ [PERSON] et al. [39] & 2024 & ARIMA, XGBoost, Random Forest, & MAE, RMSE & RMSE = 1.697 \\ [PERSON] et al. [40] & 2024 & ARIMA and LSTM & MAE, RMSE, R2 & R2 = 0.905 \\ [PERSON] et al. [41] & 2024 & Linear Regression, SVR, LSTM, & RMSE & RMSE = 10.864 \\  & & Neural Networks & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research and comparison of the prediction of EV sales. EV sales under a general category and ignore the demand dynamics among different vehicle types (BEV, HEV, PHEV) [9; 33; 34]. This prevents more detailed forecasts specific to each vehicle type. One of the innovations of the EVs-PredNet model is that it fills this gap and provides separate forecasts for BEV, HEV, PHEV, and general EV categories. In this way, market dynamics specific to each vehicle type can be analyzed more clearly and more specific sales forecasts can be made. The combination of LSTM and CNN-based deep learning methods is another important improvement. This goes beyond the linear approaches of traditional time series and machine learning models that have been used a lot in the literature [4; 30]. Many models in the literature cannot fully model nonlinear data structures and have difficulty capturing complex relationships [6; 31]. By combining the deep learning capabilities of the LSTM and CNN models, EVs-PredNet can model these nonlinear structures more effectively and achieve higher accuracy rates. For instance, [PERSON] and [PERSON] (2022) found the PCA-GRNN model's Multiple R value to be 91%, but deep learning models can further enhance this value [9]. Many studies in the literature usually evaluate the performance of prediction models with a single error metric (such as MAPE or RMSE) [9; 32]. This limited performance evaluation approach cannot fully measure the overall success of the models. EVs-PredNet, on the other hand, offers the opportunity to make a more comprehensive evaluation using multiple error metrics (MAE, MSE, RMSE, R-Squared). This increases the generalization ability of the model by testing its performance in multiple ways and provides more reliable results compared to other models.