{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T14:03:36Z","timestamp":1791900216416,"version":"4.3.4"},"reference-count":26,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB1403300"],"award-info":[{"award-number":["2018YFB1403300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51875018"],"award-info":[{"award-number":["51875018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1109\/tii.2022.3176821","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T16:51:32Z","timestamp":1653324692000},"page":"1691-1702","source":"Crossref","is-referenced-by-count":110,"title":["Short-Term Forecasting Based on Graph Convolution Networks and Multiresolution Convolution Neural Networks for Wind Power"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-740X","authenticated-orcid":false,"given":"Yue","family":"Song","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5002-9694","authenticated-orcid":false,"given":"Diyin","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1998-6995","authenticated-orcid":false,"given":"Jinsong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8863-2245","authenticated-orcid":false,"given":"Zetian","family":"Yu","sequence":"additional","affiliation":[{"name":"Latin America Office, Global Energy Interconnection Development and Cooperation Organization, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology R&amp;D Center, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TMECH.2017.2759301","article-title":"Wind turbine fault detection using a denoising autoencoder with temporal information","volume":"23","author":"guoqian","year":"2018","journal-title":"IEEE\/ASME Trans Mechatronics"},{"key":"ref11","article-title":"Telemetry data-based spacecraft anomaly detection with spatial&#x2013;temporal generative adversarial networks","volume":"70","author":"jinsong","year":"2021","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2019.00032"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3004436"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.trc.2020.01.010","article-title":"A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data","volume":"112","author":"tba","year":"2020","journal-title":"Transp Res C Emerg Technol"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.neunet.2021.05.035","article-title":"IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction","volume":"143","author":"ja","year":"2021","journal-title":"Neural Netw"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1126\/science.1205438"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117693"},{"key":"ref18","first-page":"417","article-title":"Graph neural networks for social recommendation","author":"wenqi","year":"2019","journal-title":"Proc World Wide Web Conf"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2881284"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2018.2858183"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2021.111351"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107053"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2017.04.094"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115098"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.03.044"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.117766"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/en13153764"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.05.044"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref22","article-title":"Multi-scale context aggregation by dilated convolutions","author":"yu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3059002"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"5927","DOI":"10.1109\/TII.2020.3006928","article-title":"Multi-source wind speed fusion method for short-term wind power prediction","volume":"17","author":"jianqi","year":"2021","journal-title":"IEEE Trans Ind Informat"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref26","first-page":"434","article-title":"Very short-term spatial and temporal wind power forecasting: A deep learning approach","volume":"6","author":"hu","year":"2019","journal-title":"CSEE J Power Energy Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3013166"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/9989328\/09780029.pdf?arnumber=9780029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T14:16:25Z","timestamp":1673878585000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9780029\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":26,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tii.2022.3176821","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2]]}}}