Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2306.06142 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911166678171648 |
|---|---|
| author | Amara-Ouali, Yvenn Goude, Yannig Doumèche, Nathan Veyret, Pascal Thomas, Alexis Hebenstreit, Daniel Wedenig, Thomas Satouf, Arthur Jan, Aymeric Deleuze, Yannick Berhaut, Paul Treguer, Sébastien Phe-Neau, Tiphaine |
| author_facet | Amara-Ouali, Yvenn Goude, Yannig Doumèche, Nathan Veyret, Pascal Thomas, Alexis Hebenstreit, Daniel Wedenig, Thomas Satouf, Arthur Jan, Aymeric Deleuze, Yannick Berhaut, Paul Treguer, Sébastien Phe-Neau, Tiphaine |
| contents | The transport sector is a major contributor to greenhouse gas emissions in Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix would reduce carbon emissions. However, to support the development of electric mobility, a better understanding of EV charging behaviours and more accurate forecasting models are needed. To fill that gap, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020-2021. The forecasts were evaluated at three levels of aggregation (individual stations, areas and global) to capture the inherent hierarchical structure of the data. The results highlight the potential of hierarchical forecasting approaches to accurately predict EV charging station occupancy, providing valuable insights for energy providers and EV users alike. This open dataset addresses many real-world challenges associated with time series, such as missing values, non-stationarity and spatio-temporal correlations. Access to the dataset, code and benchmarks are available at https://gitlab.com/smarter-mobility-data-challenge/tutorials to foster future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_06142 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge Amara-Ouali, Yvenn Goude, Yannig Doumèche, Nathan Veyret, Pascal Thomas, Alexis Hebenstreit, Daniel Wedenig, Thomas Satouf, Arthur Jan, Aymeric Deleuze, Yannick Berhaut, Paul Treguer, Sébastien Phe-Neau, Tiphaine Databases Machine Learning The transport sector is a major contributor to greenhouse gas emissions in Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix would reduce carbon emissions. However, to support the development of electric mobility, a better understanding of EV charging behaviours and more accurate forecasting models are needed. To fill that gap, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020-2021. The forecasts were evaluated at three levels of aggregation (individual stations, areas and global) to capture the inherent hierarchical structure of the data. The results highlight the potential of hierarchical forecasting approaches to accurately predict EV charging station occupancy, providing valuable insights for energy providers and EV users alike. This open dataset addresses many real-world challenges associated with time series, such as missing values, non-stationarity and spatio-temporal correlations. Access to the dataset, code and benchmarks are available at https://gitlab.com/smarter-mobility-data-challenge/tutorials to foster future research. |
| title | Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2306.06142 |