{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-spatio-temporal-forecasting-of","title":"Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand","arxiv_id":"2106.10940","date":"2021-06-21","proceeding":null,"authors":["Frederik Boe Hüttel","Inon Peled","Filipe Rodrigues","Francisco C. Pereira"],"abstract":"Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. 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