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Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Jiaan Liu, Chenyu Ge, Leijiao |
| Copyright Year | 2022 |
| Description | The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the forecasted load. In addition, an additional BP network maps the selected meteorological and date features into a high-dimensional feature vector, which is spliced with the output of the TCN. According to experimental results employing urban charging station load data from a city in northern China, the proposed model is more accurate than artificial neural network (ANN), long short-term memory (LSTM), convolutional neural networks and long short-term memory (CNN-LSTM), and TCN models. The MCCNN-TCN model outperforms the ANN, LSTM, CNN-LSTM, and TCN by 14.09%, 25.13%, 27.32%, and 4.48%, respectively, in terms of the mean absolute percentage error. |
| Starting Page | 2633 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15072633 |
| Journal | Energies |
| Issue Number | 7 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-04-04 |
| Access Restriction | Open |
| Subject Keyword | Energies Transportation Science and Technology Electric Vehicle Short-term Load Forecasting Convolutional Neural Network Temporal Convolutional Network Climate Factors Correlation Analysis |
| Content Type | Text |
| Resource Type | Article |
