# Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques

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## Abstract

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## 1. Introduction

## 2. Literature Review

## 3. Materials and Methods

#### 3.1. Data Collection

#### 3.2. Data Preparation

#### 3.2.1. Data Transformation and Aggregation

#### 3.2.2. Data Analysis

#### 3.2.3. Outliers Removal

#### 3.2.4. Normalization

#### 3.3. Feature Addition, Correlation Analysis, and Feature Selection

#### 3.3.1. Autocorrelation Plot

#### 3.3.2. Correlation Matrix

#### 3.3.3. Data Partitioning

#### 3.4. Algorithms and Implementation

#### 3.4.1. Auto-Regressive (AR) and Auto-Regressive Exogenous (ARX) Forecasting

#### 3.4.2. SVR

#### 3.4.3. LSTM

#### 3.4.4. Implementation

- (a)
- AR and ARX: The AR model is univariate, considering the target-predicted variable as the only feature. Here, the only feature considered is ‘AggregatedPower’. ForecasterAutoreg class in sklearn package is used to implement the regression model. Among the various regressors, Ridge regressor is found to present the best performance on the dataset. A lag parameter of 48 is decided, meaning that output at each step depends on the previous 48 steps. ARX is multivariate, where it considers multiple attributes for prediction. The model considers ‘ConnectionHour’, ‘WorkingStatus’, ‘HourlyAverageDemand’, and ‘Previous24HrAverageDemand’ features as exogenous variables along with the target variable ‘AggregatedPower’. The model uses a Ridge regressor and a lag of 48.
- (b)
- SVR: In the SVR model with RBF kernel, the attributes ‘ConnectionHour’, ‘WorkingStatus’, ‘HourlyAverageDemand’, and ‘Previous24HrAverageDemand’ are considered as the independent features and ‘AggregatedPower’ as the target. The best values for hyperparameters are obtained using grid search. Regularization parameter (C) is obtained as ten and gamma as one.
- (c)
- LSTM: Multivariate LSTM model is considered where the features are ‘ConnectionHour’, ‘WorkingStatus’, ‘HourlyAverageDemand’, ‘Previous24HrAverageDemand’, and ‘AggregatedPower’. A step size of 48 is chosen after trial and error, which means, the output power at any step is influenced by the values of these features for the previous 48 steps. Multiple LSTM configurations were tried, and performance was evaluated. Finally, the model having one LSTM layer of 50 neurons and tanh activation, followed by a fully connected layer with 50 neurons and ReLu activation and a single neuron at the output layer, is chosen as the best model. The configurations chosen for the models are given in Table 2.

#### 3.4.5. Performance Metrics

- (a)
- ME:

- (b)
- MAE:

- (c)
- RMSE:

- (d)
- MAPE:

## 4. Results and Discussions

#### 4.1. Prediction on a Normal Weekend

#### 4.2. Prediction on a Normal Weekday

#### 4.3. Prediction on a Weekday Holiday

## 5. Conclusions and Future Scope of Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Average power demand for each day of the week shows the difference in a pattern during weekdays and weekends.

**Figure 5.**Correlation matrix showing correlation among the features and between dependent and independent features.

**Figure 10.**Demand prediction for 16 January, a normal working day, showing the models following the actual demand closely.

Feature | Description |
---|---|

ConnectionHour | Hour of the day at which the vehicle is connected to the charging port |

DayofWeek | Day number e.g., 0—Sunday, 1—Monday |

Week | Week number of the year |

WorkingStatus | Binary value, indicating working day or holiday: 1 for working day, 0 for holiday |

HourlyAverageDemand | Average power demand for the given connection hour |

Previous24HrAverageDemand | Average demand for the previous 24 h |

AggregatedPower | EV charging demand (kW) |

Model | Parameter | Value |
---|---|---|

ARF | Regressor | Ridge |

Lags | 48 | |

Kernel | RBF | |

SVR | C | 10 |

Gamma | 1 | |

Epochs | 30 | |

LSTM | Batch Size | 192 |

Optimizer | Adam | |

Learning Rate | 0.001 | |

Output Activation Function | Linear |

Performance Metric | AR | ARX | SVR | LSTM |
---|---|---|---|---|

ME (kW) | 63.2 | 42 | 43 | 24 |

MAE (kW) | 8.5 | 7.4 | 7.7 | 4.2 |

RMSE (kW) | 12.6 | 10.1 | 10.5 | 5.9 |

MAPE * | 0.73 | 0.86 | 0.71 | 0.4 |

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## Share and Cite

**MDPI and ACS Style**

Vishnu, G.; Kaliyaperumal, D.; Pati, P.B.; Karthick, A.; Subbanna, N.; Ghosh, A.
Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques. *World Electr. Veh. J.* **2023**, *14*, 266.
https://doi.org/10.3390/wevj14090266

**AMA Style**

Vishnu G, Kaliyaperumal D, Pati PB, Karthick A, Subbanna N, Ghosh A.
Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques. *World Electric Vehicle Journal*. 2023; 14(9):266.
https://doi.org/10.3390/wevj14090266

**Chicago/Turabian Style**

Vishnu, Gayathry, Deepa Kaliyaperumal, Peeta Basa Pati, Alagar Karthick, Nagesh Subbanna, and Aritra Ghosh.
2023. "Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques" *World Electric Vehicle Journal* 14, no. 9: 266.
https://doi.org/10.3390/wevj14090266