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A realistic dataset generator for smart grid ecosystems with electric vehicles

Charalampidis Georgios, Akasiadis Charilaos, Rigas Emmanouil S., Chalkiadakis Georgios

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URIhttp://purl.tuc.gr/dl/dias/53066E80-3347-454A-AA29-97B0D36DEAB1-
Identifierhttps://doi.org/10.1145/3538637.3538755-
Identifierhttps://dl.acm.org/doi/10.1145/3538637.3538755-
Languageen-
Extent2 pagesen
TitleA realistic dataset generator for smart grid ecosystems with electric vehiclesen
CreatorCharalampidis Georgiosen
CreatorΧαραλαμπιδης Γεωργιοςel
CreatorAkasiadis Charilaosen
CreatorRigas Emmanouil S.en
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
PublisherAssociation for Computing Machinery (ACM)en
Content SummaryResearch on the deployment and employment of electric vehicles (EVs) in the emerging Smart Grid, typically requires access to large datasets containing data that is rich and reliable. Such datasets are hard to come by in the wild due to various privacy and sensitivity considerations. In this paper, we design a dataset generator for large-scale EVs charging management. The generator (i) takes as input anonymized real-world datasets describing different energy generation and demand types, as well as charging profiles of EVs and corresponding trip and type information; (ii) fits a variety of machine learning models using this data as training sets; and (iii) generates new synthetic data that adheres to the same principles and relationships as the input. The generator comes complete with data smoothing and dataset summarization, visualization, and comparison abilities that users can utilize via a web-based interface; and is offered as a free-to-use tool to the research community.en
Type of ItemΑφίσα σε Συνέδριοel
Type of ItemConference Posteren
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-09-20-
Date of Publication2022-
SubjectDataset generatoren
SubjectElectric vehiclesen
SubjectChargingen
SubjectSmart griden
Bibliographic CitationG. Charalambidis, C. Akasiadis, E. S. Rigas and G. Chalkiadakis, “A realistic dataset generator for smart grid ecosystems with electric vehicles,” in Proceedings of the 13th ACM International Conference on Future Energy Systems (e-Energy 2022), virtual event, 2022, pp. 432–433, June 2022, doi: 10.1145/3538637.3538755.en

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