With the rapid growth of electric vehicle adoption in China, driven by environmental policies and technological advancements, the demand for accurate load forecasting has become critical for grid stability and management. The inherent volatility and uncertainty in electric vehicle charging patterns pose significant challenges to traditional prediction methods. In this paper, I propose an enhanced load forecasting model for electric vehicles that integrates an improved Long Short-Term Memory (LSTM) neural network with Analytic Hierarchy Process (AHP) and Domain Adversarial Neural Network (DANN) techniques. This approach aims to address the limitations of existing models in handling small-sample datasets and complex influencing factors, thereby improving prediction accuracy and robustness for China EV applications.
The proliferation of electric vehicles in urban areas, such as major Chinese cities, has led to increased stress on power grids during peak charging periods. Factors like energy supply modes, temperature variations, charging behaviors, and battery characteristics contribute to the nonlinear and time-dependent nature of electric vehicle load profiles. Traditional models, including basic artificial neural networks (ANNs) and standard LSTM networks, often struggle to capture these dynamics effectively, especially when data is scarce. My model leverages AHP to quantitatively assess the weight of each influencing factor, ensuring that the input features for the neural network are objectively prioritized. Subsequently, the LSTM network is optimized using DANN to enhance feature extraction and generalization capabilities, making it suitable for real-world scenarios with limited historical data.
To establish a comprehensive framework for electric vehicle load forecasting, I first analyze the key factors affecting load patterns using the Analytic Hierarchy Process. This method allows for a structured evaluation of multiple criteria, such as energy supply modes, environmental temperature, charging patterns, and battery properties. By constructing a judgment matrix and calculating consistency indices, I derive the relative weights of these factors, which are then integrated into the neural network model. The overall structure of the forecasting system involves data preprocessing, feature weighting via AHP, LSTM-based sequence prediction, and DANN-driven domain adaptation to minimize discrepancies between source and target data distributions. This holistic approach ensures that the model accounts for both temporal dependencies and external influences specific to China EV ecosystems.
The core of the forecasting model relies on an improved LSTM neural network, which is adept at handling sequential data and mitigating gradient vanishing issues. The LSTM architecture comprises three gating mechanisms: forget gate, input gate, and output gate. Each gate regulates the flow of information through sigmoid and tanh activation functions, enabling the network to retain long-term dependencies. For instance, the forget gate determines the extent to which previous hidden states are discarded, as defined by the following equation:
$$f_t = \sigma(W_f \cdot [y_{t-1}, x_t] + p_f)$$
Here, $f_t$ represents the forget gate output, $\sigma$ denotes the sigmoid function, $W_f$ is the weight matrix, $y_{t-1}$ is the previous output, $x_t$ is the current input, and $p_f$ is the bias term. Similarly, the input gate computes the candidate state and update value using:
$$r_t = \sigma(W_r \cdot [y_{t-1}, x_t] + p_r)$$
$$\tilde{O}_t = \tanh(W_o \cdot [y_{t-1}, x_t] + p_o)$$
$$O_t = f_t * O_{t-1} + r_t * \tilde{O}_t$$
where $r_t$ is the input gate activation, $\tilde{O}_t$ is the candidate memory cell, and $O_t$ is the updated cell state. The output gate then generates the final prediction:
$$q_t = \sigma(W_q \cdot [y_{t-1}, x_t] + p_q)$$
$$y_t = q_t * \tanh(O_t)$$
These equations form the basis of the LSTM network, which I enhance by incorporating AHP-derived weights into the input features. For example, the weight assigned to battery characteristics (45.929%) significantly influences the input vector, ensuring that critical factors are emphasized during training. This integration improves the model’s sensitivity to key electric vehicle parameters, leading to more accurate load forecasts for China EV scenarios.
To address the challenge of limited data availability, I incorporate a Domain Adversarial Neural Network into the LSTM framework. DANN acts as a feature extractor that aligns the distributions of source and target domains, reducing domain shift and enhancing model robustness. The gradient reversal layer in DANN ensures that the feature representations are invariant to domain differences, thereby improving generalization. The combined loss function for the model is expressed as:
$$S_d = \eta_2(D_s, D_t) + Q(D_s, y_s)$$
where $\eta_2(D_s, D_t)$ quantifies the discrepancy between source domain $D_s$ (e.g., historical electric vehicle load data) and target domain $D_t$ (e.g., new or unseen data), and $Q(D_s, y_s)$ represents the classification error. By minimizing this loss, the model learns to extract transferable features that are resilient to variations in data distribution, which is crucial for adapting to evolving China EV infrastructures.
The influencing factors for electric vehicle load forecasting are diverse and interconnected. Through AHP, I evaluate these factors and assign weights based on pairwise comparisons. The judgment matrix $H$ is constructed as follows:
$$H = \begin{bmatrix}
h_{11} & h_{12} & \cdots & h_{1m} \\
h_{21} & h_{22} & \cdots & h_{2m} \\
\vdots & \vdots & \ddots & \vdots \\
h_{m1} & h_{m2} & \cdots & h_{mm}
\end{bmatrix}$$
where $h_{ab}$ indicates the relative importance of factor $a$ compared to factor $b$. The priority vector is computed by normalizing the matrix and calculating the eigenvector corresponding to the largest eigenvalue. The consistency ratio is checked to ensure the validity of the comparisons. The resulting weights for the key factors are summarized in the table below:
| Influencing Factor | Weight |
|---|---|
| Energy Supply Mode | 32.679% |
| Temperature Environment | 7.921% |
| Charging Pattern | 13.471% |
| Battery Characteristics | 45.929% |
These weights are embedded into the LSTM input layer, allowing the model to prioritize high-impact factors during training. For instance, battery characteristics, which account for nearly half of the total weight, dominate the feature vector, reflecting their significance in determining electric vehicle load patterns in China.
For experimental validation, I utilize real-world datasets from a Chinese urban area, specifically focusing on electric vehicle load data from 2022 to 2023. The dataset is split into 70% for training and 30% for testing, with preprocessing steps including normalization and sequence segmentation. The model is implemented in a PyTorch environment, and performance is evaluated using root mean square error (RMSE) and standard deviation metrics. Comparative analyses are conducted against baseline models, such as standard LSTM and ANN, to demonstrate the superiority of the proposed approach.
The forecasting results indicate that the improved LSTM model with AHP and DANN integration achieves higher accuracy and stability. For example, the predicted load values closely align with actual observations, as shown in the following comparative analysis. The standard deviation of the RMSE for the proposed model remains within 2.7% to 3.7%, outperforming the baseline models which exhibit greater fluctuations. This consistency is critical for grid operators managing electric vehicle charging infrastructure in China.
To further illustrate the model’s performance, I present a table summarizing the monthly average standard deviations of prediction errors over a five-month period:
| Model | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 |
|---|---|---|---|---|---|
| Proposed Improved LSTM | 3.1% | 2.9% | 3.3% | 3.0% | 3.2% |
| Standard LSTM | 4.5% | 4.8% | 5.1% | 4.7% | 4.9% |
| ANN | 6.2% | 6.5% | 6.8% | 6.4% | 6.6% |
As evident from the table, the proposed model maintains lower error standard deviations across all months, highlighting its robustness in handling temporal variations in electric vehicle load data. This is attributed to the effective weighting of influencing factors and domain adaptation, which collectively enhance prediction reliability for China EV applications.
In conclusion, the integration of AHP-weighted features and DANN-based optimization into an LSTM neural network provides a powerful solution for electric vehicle load forecasting. The model not only improves accuracy but also demonstrates strong generalization capabilities in small-sample scenarios, which are common in emerging China EV markets. Future work could explore the incorporation of real-time data streams and additional environmental factors to further refine predictions. This research contributes to the sustainable development of electric vehicle ecosystems by enabling more efficient grid planning and management.
The mathematical formulation of the AHP process involves calculating the consistency index $CI$ and consistency ratio $CR$ to validate the judgment matrix. For a matrix of order $m$, $CI$ is given by:
$$CI = \frac{\lambda_{\text{max}} – m}{m – 1}$$
where $\lambda_{\text{max}}$ is the maximum eigenvalue. The $CR$ is then computed as $CR = CI / RI$, where $RI$ is the random index. If $CR < 0.1$, the matrix is considered consistent. In my model, this ensures that the assigned weights for electric vehicle factors are reliable and objectively derived.
Additionally, the LSTM training process involves minimizing the mean squared error (MSE) loss function:
$$\mathcal{L} = \frac{1}{N} \sum_{i=1}^{N} (y_i – \hat{y}_i)^2$$
where $N$ is the number of samples, $y_i$ is the actual load, and $\hat{y}_i$ is the predicted load. The incorporation of DANN modifies this loss to include domain adaptation terms, as previously described. This comprehensive approach ensures that the model remains adaptive to the dynamic nature of electric vehicle load patterns in China.
The proliferation of electric vehicles in China necessitates advanced forecasting tools to mitigate grid instability. My model addresses this by leveraging neural networks and multi-criteria decision-making, providing a scalable framework for other regions. As the adoption of electric vehicles continues to rise, such innovations will play a pivotal role in shaping smart grid infrastructures and promoting sustainable transportation solutions.