#
Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### Research Gap and Contribution

## 3. Materials and Methods

#### 3.1. Data Pre-Processing

#### 3.1.1. Data Transformation

#### 3.1.2. Data Partitioning and Normalization

#### 3.2. Neural Networks

#### 3.2.1. Technical Background

#### 3.2.2. Features

#### 3.2.3. Final Configuration

#### 3.3. Forecast Post-Processing

#### 3.4. Model Performance Evaluation

## 4. Results and Discussions

#### 4.1. Use Case and Data Analysis

#### 4.2. Simulation Results

#### 4.2.1. Neural Network Convergence Analysis

#### 4.2.2. Two-Weeks Period Forecast Example

#### 4.2.3. Test Subset Performances

#### 4.2.4. Feature Importance

#### 4.3. Forecast Timing and Real-Time Capabilities

^{®}Xeon

^{®}E-2176M processor with 64 Gb of installed RAM. Table 4 shows the time to train together with the validation of the neural networks and the time to test 50 day-ahead forecasts with the average time to test one day-ahead forecast.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

EMS | Energy management system |

EV | Electric vehicle |

MAE | Mean-absolute error |

MSE | Mean-square error |

LSTM | Long short-term memory |

LSTM-B | Long short-term memory-Base |

LSTM-C | Long short-term memory-Calendar |

LSTM-W | Long short-term memory-Weather |

Relu | Rectified linear unit |

RMSE | Root-mean-square error |

RNN | Recurrent neural network |

Tanh | Hyperbolic tangent |

VIANN | Variance-based feature Importance in Artificial Neural Networks |

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**Figure 4.**Yearly EV charging demand summarized to one day time where the mean, quartiles, and maximum values are shown.

**Figure 5.**Weekly EV charging demand. Different colors are given for the training, validation, and test dataset.

Class | Feature | LSTM-B | LSTM-C | LSTM-W |
---|---|---|---|---|

Load | EV charging demand [kW] | X | X | X |

Average weekly EV demand [kW] | X | X | ||

Calendar | Quarter-hour number [/] | X | X | |

Day number [/] | X | X | ||

Binary working day [0 or 1] | X | X | ||

Binary Holiday [0 or 1] | X | X | ||

Weather | Daily temperature [${}^{\circ}C$] | X | ||

Daily rainfall [mm/h] | X |

Parameters | LSTM-B | LSTM-C | LSTM-W |
---|---|---|---|

Epochs | 30 | 50 | 50 |

Batch size | 512 | 192 | 192 |

Optimizer | RMSprop | Adam | Adam |

Loss function | MAE | MSE | MSE |

Learning rate | 0.001 | 0.001 * | 0.001 * |

Hidden neurons | 16 | 25 ** | 30 ** |

Activation function | Tanh | Tanh ** | Tanh ** |

Dropout | 0.3 | 0 ** | 0.3 ** |

Metrics | LSTM-B | LSTM-C | LSTM-W |
---|---|---|---|

MAE | 1.25 kW | 0.96 kW (−23.2%) | 0.89 kW (−28.8%) |

RMSE | 2.29 kW | 1.85 kW (−19.22%) | 1.92 kW (−16.16%) |

Neural Networks | Training and Validation [min] | Testing 50 Days [s] | Average Time for a Day-Ahead Forecast [s] |
---|---|---|---|

LSTM-B | 3.88 | 3.17 | 0.063 |

LSTM-C | 11.47 | 3.42 | 0.068 |

LSTM-W | 15.04 | 3.28 | 0.065 |

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**MDPI and ACS Style**

Van Kriekinge, G.; De Cauwer, C.; Sapountzoglou, N.; Coosemans, T.; Messagie, M.
Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks. *World Electr. Veh. J.* **2021**, *12*, 178.
https://doi.org/10.3390/wevj12040178

**AMA Style**

Van Kriekinge G, De Cauwer C, Sapountzoglou N, Coosemans T, Messagie M.
Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks. *World Electric Vehicle Journal*. 2021; 12(4):178.
https://doi.org/10.3390/wevj12040178

**Chicago/Turabian Style**

Van Kriekinge, Gilles, Cedric De Cauwer, Nikolaos Sapountzoglou, Thierry Coosemans, and Maarten Messagie.
2021. "Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks" *World Electric Vehicle Journal* 12, no. 4: 178.
https://doi.org/10.3390/wevj12040178