Forecasting BEV charging station occupancy at work places
| dc.contributor.author | Motz, Marvin | |
| dc.contributor.author | Huber, Julian | |
| dc.contributor.author | Weinhardt, Christof | |
| dc.contributor.editor | Reussner, Ralf H. | |
| dc.contributor.editor | Koziolek, Anne | |
| dc.contributor.editor | Heinrich, Robert | |
| dc.date.accessioned | 2021-01-27T13:34:12Z | |
| dc.date.available | 2021-01-27T13:34:12Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | At many charging stations, the charging process of battery electric vehicles (BEV) takes significantly more time than refilling a gas tank. In combination with the lack of charging stations, this results in more planning effort for drivers who have to find a free charging station. In addition, charging draws a significant amount of energy from the power grid so that operators might have to coordinate charging to avoid congestion. Such problems are especially relevant at workplaces, where the employer might offer many charging stations for employees with similar working hours. An approach to overcome these problems lies in the management of the existing infrastructure using data-driven strategies. Accurate forecasts on the occupancy of charging stations allow allocating available resources more efficiently. This work aims to find suitable methods to predict the occupancy of single charging stations, given their historical data. The forecasts could be used as an input in decision support for drivers or energy management systems of charging station operators. This paper discusses feature importance, transferability between multiple charging stations at one location, and how the characteristics of charging stations influence the predictability of their occupancy. We use 52 charging stations from the open ACN data set to evaluate the research questions. The data set has more than 24,000 charging events and is located at a research facility so that it resembles workplace parking. Finally, we test the forecast on new, previously unseen data to ensure the findings hold-up in a realistic scenario. | en |
| dc.identifier.doi | 10.18420/inf2020_68 | |
| dc.identifier.isbn | 978-3-88579-701-2 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/34779 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2020 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-307 | |
| dc.subject | Battery Electric Vehicles | |
| dc.subject | Charging | |
| dc.subject | Forecasting | |
| dc.title | Forecasting BEV charging station occupancy at work places | en |
| gi.citation.endPage | 781 | |
| gi.citation.startPage | 771 | |
| gi.conference.date | 28. September - 2. Oktober 2020 | |
| gi.conference.location | Karlsruhe | |
| gi.conference.sessiontitle | Herausforderungen zukünftiger cyber-physischer Energiesysteme |
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