Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 3328-3333 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781728191423 |
| ISBN (Print) | 978-1-7281-9143-0 |
| Publikationsstatus | Veröffentlicht - 19 Sept. 2021 |
| Veranstaltung | 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, USA / Vereinigte Staaten Dauer: 19 Sept. 2021 → 22 Sept. 2021 |
Publikationsreihe
| Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
|---|---|
| Band | 2021-September |
Abstract
With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effectively forecast the charging station occupation. We exploit static information, such as the mean occupation concerning the time of day, to learn the specific charging station patterns. We supplement such static data with dynamic information reflecting the preceding charging station occupation and temporal information such as daytime and weekday. Our model efficiently fuses dynamic and static information to facilitate accurate forecasting. We evaluate the proposed model on a real-world dataset containing 593 charging stations in Germany, covering August 2020 to December 2020. Our experiments demonstrate that DFDS outperforms the baselines by 3.45 percent points in F1-score on average.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
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- BibTex
- RIS
2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 3328-3333 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Band 2021-September).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting
AU - Sao, Ashutosh
AU - Tempelmeier, Nicolas
AU - Demidova, Elena
N1 - Funding Information: This work is partially funded by the BMWi, Germany under “d-E-mand” (grant ID 01ME19009B) and “CampaNeo” (grant ID 01MD19007B), the European Commission (EU H2020, “smashHit”, grant-ID 871477), and DFG, German Research Foundation (“WorldKG”, DE 2299/2-1).
PY - 2021/9/19
Y1 - 2021/9/19
N2 - With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effectively forecast the charging station occupation. We exploit static information, such as the mean occupation concerning the time of day, to learn the specific charging station patterns. We supplement such static data with dynamic information reflecting the preceding charging station occupation and temporal information such as daytime and weekday. Our model efficiently fuses dynamic and static information to facilitate accurate forecasting. We evaluate the proposed model on a real-world dataset containing 593 charging stations in Germany, covering August 2020 to December 2020. Our experiments demonstrate that DFDS outperforms the baselines by 3.45 percent points in F1-score on average.
AB - With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effectively forecast the charging station occupation. We exploit static information, such as the mean occupation concerning the time of day, to learn the specific charging station patterns. We supplement such static data with dynamic information reflecting the preceding charging station occupation and temporal information such as daytime and weekday. Our model efficiently fuses dynamic and static information to facilitate accurate forecasting. We evaluate the proposed model on a real-world dataset containing 593 charging stations in Germany, covering August 2020 to December 2020. Our experiments demonstrate that DFDS outperforms the baselines by 3.45 percent points in F1-score on average.
UR - http://www.scopus.com/inward/record.url?scp=85118473082&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2108.12352
DO - 10.48550/arXiv.2108.12352
M3 - Conference contribution
AN - SCOPUS:85118473082
SN - 978-1-7281-9143-0
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 3328
EP - 3333
BT - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Y2 - 19 September 2021 through 22 September 2021
ER -