Journal
E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS
Volume -, Issue -, Pages 139-149Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3307772.3328313
Keywords
Electric vehicle charging; open dataset; user behavior prediction; workplace charging; on-site solar generation; duck curve
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Funding
- NSF [CCF-1637598, ECCS-1619352, CNS-1545096, CPS-1739355, DGE-1745301]
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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.
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