{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T07:08:29Z","timestamp":1782803309928,"version":"3.54.5"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council (ARC) Discovery Early Career Researcher Award","doi-asserted-by":"publisher","award":["DE230100046"],"award-info":[{"award-number":["DE230100046"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Smart Grid"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tsg.2025.3633777","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T18:41:43Z","timestamp":1763404903000},"page":"1430-1443","source":"Crossref","is-referenced-by-count":3,"title":["A Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4812-3320","authenticated-orcid":false,"given":"Jinhao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University, Melbourne, VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5182-7938","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University, Melbourne, VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1088\/2516-1083\/ad6be1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s44287-023-00004-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.122972"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-35393-0"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2829917"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115063"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2024.3364579"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3180399"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124308"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3054763"},{"key":"ref11","first-page":"1","article-title":"Deep spatio-temporal forecasting of electrical vehicle charging demand","volume-title":"Proc. Tackling Climate Change Mach. Learn. Workshop Int. Conf. Mach. Learn.","author":"H\u00fcttel"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3321116"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2022.108651"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3368419"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2022.3192285"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2023.3311795"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2023.104205"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3182972"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3276947"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2024.3449339"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3538637.3538850"},{"key":"ref22","volume-title":"Ev Owner Demographics and Behaviours","year":"2023"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/0098-3004(84)90020-7"},{"key":"ref24","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref27","first-page":"1","article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Oreshkin"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref29","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. 35th Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref30","first-page":"27268","article-title":"FedFormer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25854"},{"key":"ref33","first-page":"1","article-title":"Graph wavelet neural network","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref34","first-page":"1509","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"32","author":"Yadati"},{"key":"ref35","volume-title":"Electric Vehicle Charging Station Usage","year":"2021"},{"key":"ref36","volume-title":"EV Charge Station Use","year":"2023"},{"key":"ref37","volume-title":"Electric Vehicle Charging Station Data","year":"2023"},{"key":"ref38","volume-title":"Electric Vehicle Charging Station Data","year":"2023"},{"issue":"1","key":"ref39","article-title":"Package imputeTS","volume":"9","author":"Moritz","year":"2019"},{"issue":"1","key":"ref40","article-title":"NeuralForecast: User friendly state-of-the-art neural forecasting models","volume":"37","author":"Olivares","year":"2022"},{"key":"ref41","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Kipf"}],"container-title":["IEEE Transactions on Smart Grid"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5165411\/11404354\/11251047.pdf?arnumber=11251047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T20:48:28Z","timestamp":1771879708000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11251047\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":41,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tsg.2025.3633777","relation":{},"ISSN":["1949-3053","1949-3061"],"issn-type":[{"value":"1949-3053","type":"print"},{"value":"1949-3061","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}