# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 3 Proposed Methodology
### Lstm  
In 1997, [PERSON] and [PERSON] proposed the LSTM deep learning technique, a type of recurrent neural network, as a solution to the constraints of the transient stability assessment (TSA) architecture. LSTM can be used for purposes including prediction, classification, and analyzing of time-series data [32; 42]. The LSTM model is a recursive neural network that incorporates a gating mechanism to control the information flow within the network. This gating method is based on a simple recursive neural network [43; 44; 45; 46]. Figure 2 presents the architecture of the LSTM model. In the distinctive cellular structure of the LSTM model, the forget gate is responsible for selecting and removing the cell state from the previous sequence. The previous hidden state in the time series, labeled as \(h_{t-1}\), and the current input in the time series, labeled as \(x_{t}\), undergo processing by the activation function \(\sigma_{g}\) to generate the output vector \(f_{t}\) of the forget gate. \[f_{t}=\sigma_{g}\ \left(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\right), \tag{1}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Type** & **Output Shape** & **Param** \\ \hline conv1d\_1 & Conv1D & (None, 8,8) & 64 \\ conv1d\_2 & Conv1D & (None, 6,16) & 1568 \\ max\_pooling1d & MaxPooling1D & (None, 3,16) & 0 \\ lstm & LSTM & (None, 50) & 16,600 \\ flatten & Flatten & (None, 50) & 0 \\ dense & Dense & (None, 500) & 25,500 \\ dropout & Dropout & (None, 500) & 0 \\ dense\_1 & Dense & (None, 200) & 100,200 \\ dense\_2 & Dense & (None, 1) & 201 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The structure of the proposed EVs-PredNet architecture. Figure 1: The schema of the proposed EVs-PredNet regression model. The forget gate's weight coefficients are labeled as \(W_{f}\) and \(U_{f}\), with the bias coefficient indicated as \(b_{f}\). The activation function is denoted by \(\sigma_{g}\). The input gate controls the movement of input data into the currently active cell. The coefficients \(i_{t}\) and \(C^{\prime}_{t}\) in this gate are calculated using the hidden state \(h_{t-1}\) from the previous time step and the current time series input \(x_{t}\). The coefficients determine how much information is updated and maintain the cell candidate information. These coefficients are determined by using the activation function. \[i_{t}=\sigma_{g}\ (W_{i}x_{t}+U_{i}h_{t-1}+b_{i}), \tag{2}\]  
\[C_{t}=\sigma_{c}\ (W_{c}x_{t}+U_{c}h_{t-1}+b_{c}), \tag{3}\]  
The weight coefficients are represented by the symbols \(W_{i},\ U_{i},\ W_{c}\), _and_\(U_{c}\). The bias coefficients are denoted as \(b_{i}\) and \(b_{c}\), and the activation functions are represented by \(\sigma_{g}\) and \(\sigma_{c}\) in Equations (2) and (3). The cell state is updated using a linear interaction process, where the previous cell state is modified and then integrated into the current cell state to complete the information update. The cell state, \(C_{t}\), is updated by multiplying the output of the forget gate, with the previous cell state, \(C_{t-1}\), and adding the product of the output of the input gate,\(i_{t}\), and the cell candidate data, \(C^{\prime}_{t}\). The calculation yields the new cell state, \(C_{t}\). \[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot C_{t}, \tag{4}\]  
where. denotes element-wise multiplication. The output gate computes and sends information to the next time step. The output vector \(o_{t}\) is obtained by applying the activation function \(\sigma_{g}\) to the input vectors \(h_{t-1}\) and \(x_{t}\). \[o_{t}=\sigma_{g}\ (W_{o}x_{t}+U_{o}h_{t-1}+b_{o}), \tag{5}\]  
\[h_{t}=o_{t}.tanh\ (C_{t}), \tag{6}\]  
The weight coefficients for the cell state, labeled as \(W_{o}\) and \(U_{o}\), along with the bias coefficient, labeled as \(b_{o}\), are linked to the input gate. The output gate generates the output \(o_{t}\), which is subsequently multiplied by the current sequence cell state \(C_{t}\). The hyperbolic tangent activation function (tan\({}_{\rm h}\)) processes the product to yield the final output of the hidden layer.