Published June 2019
| Version public
Book Section - Chapter
ACN-Data: Analysis and Applications of an Open EV Charging Dataset
Creators
Abstract
We are releasing ACN-Data, a dynamic dataset of workplace EV charging which currently includes over 30,000 sessions with more added daily. In this paper we describe the dataset, as well as some interesting user behavior it exhibits. To demonstrate the usefulness of the dataset, we present three examples, learning and predicting user behavior using Gaussian mixture models, optimally sizing on-site solar generation for adaptive electric vehicle charging, and using workplace charging to smooth the net demand Duck Curve.
Additional Information
© 2019 ACM. This dataset would not be possible without the combined efforts of PowerFlex Systems, Caltech Facilities, and JPL Facilities. Specifically we would like to thank Ted Lee, Cheng Jin and George Lee of PowerFlex who were instrumental to collecting this dataset as well as Caltech students Sophia Coplin and Garret Sullivan who worked on cleaning the dataset and developing tools to make it more accessible.. This material is based upon work supported by the NSF Graduate Research Fellowship (DGE-1745301) and NSF grants CCF-1637598, ECCS-1619352, CNS-1545096, and CPS-1739355.Additional details
Identifiers
- Eprint ID
- 96442
- Resolver ID
- CaltechAUTHORS:20190614-091205559
Funding
- NSF Graduate Research Fellowship
- DGE-1745301
- NSF
- CCF-1637598
- NSF
- ECCS-1619352
- NSF
- CNS-1545096
- NSF
- ECCS-1739355
Dates
- Created
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2019-06-14Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field