{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:35:54Z","timestamp":1784550954243,"version":"3.55.0"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51977042"],"award-info":[{"award-number":["51977042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Project of Guangxi Graduate Education","award":["YCBZ2019003"],"award-info":[{"award-number":["YCBZ2019003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/tnnls.2021.3106968","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T19:58:26Z","timestamp":1630699106000},"page":"2584-2593","source":"Crossref","is-referenced-by-count":46,"title":["Combination of Manifold Learning and Deep Learning Algorithms for Mid-Term Electrical Load Forecasting"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0395-9578","authenticated-orcid":false,"given":"Jinghua","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1162-2353","authenticated-orcid":false,"given":"Shanyang","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6815-6536","authenticated-orcid":false,"given":"Wei","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.09.190"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2740318"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2268"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"49144","DOI":"10.1109\/ACCESS.2018.2867681","article-title":"Empirical mode decomposition based deep learning for electricity demand forecasting","volume":"6","author":"jatin","year":"2018","journal-title":"IEEE Access"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106390"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2007.907583"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117087"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2485943"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2016.10.056"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2016.0340"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2016.08.031"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2008.926091"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2016.08.058"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2496947"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2011.0009"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2985720"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11741-004-0051-1"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2010.2042471"},{"key":"ref25","first-page":"2469","article-title":"Deep reinforcement learning for sequence-to-sequence models","volume":"31","author":"keneshloo","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780317"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2016.06.003"},{"key":"ref27","article-title":"A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting","author":"dudek","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.027"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2011.2161780"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2013.07.020"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2005.06.006"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2017.09.011"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2019.0797"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/s41601-020-00163-x"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115564"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10114428\/09528900.pdf?arnumber=9528900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T17:54:01Z","timestamp":1684778041000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9528900\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":33,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3106968","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]}}}