{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:18:10Z","timestamp":1784546290230,"version":"3.55.0"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801315"],"award-info":[{"award-number":["61801315"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51876015"],"award-info":[{"award-number":["51876015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2981817","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T21:28:26Z","timestamp":1584566906000},"page":"55785-55797","source":"Crossref","is-referenced-by-count":179,"title":["A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6479-1233","authenticated-orcid":false,"given":"Ye","family":"Hong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1129-0213","authenticated-orcid":false,"given":"Yingjie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9365-3788","authenticated-orcid":false,"given":"Qibin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8396-6825","authenticated-orcid":false,"given":"Wenzheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4703-9530","authenticated-orcid":false,"given":"Xiujuan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600114"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2016.2570603"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/en12061140"},{"key":"ref32","first-page":"59","article-title":"REDD: A public data set for energy disaggregation research","volume":"25","author":"kolter","year":"2011","journal-title":"Proc SustKDD"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2018.8377010"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.1007\/b97391","volume":"2","author":"brockwell","year":"2002","journal-title":"Introduction to Time Series and Forecasting"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2307349"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/en11123433"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5194\/gmdd-7-1525-2014"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2753802"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2016.06.003"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICRCCS.2009.12"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2008.05.008"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2003.811010"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.01.127"},{"key":"ref15","first-page":"22","article-title":"Day-ahead load forecasting using support vector regression machines","volume":"9","author":"clark","year":"2018","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2014.07.065"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.41"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.10.119"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2015.2509478"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2513900"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2504502"},{"key":"ref27","first-page":"1153","article-title":"Energy disaggregation via discriminative sparse coding","author":"kolter","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2008.04.002"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2011.06.003"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2016.2604389"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2015.2414880"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2895604"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2888978"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.07.132"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2937222"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.07.045"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2014.07.064"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/186\/5\/012042"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"5196","DOI":"10.1109\/TSG.2017.2683461","article-title":"Shape-based approach to household electric load curve clustering and prediction","volume":"9","author":"teeraratkul","year":"2017","journal-title":"IEEE Trans Smart Grid"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"5271","DOI":"10.1109\/TSG.2017.2686012","article-title":"Deep learning for household load forecasting—A novel pooling deep RNN","volume":"9","author":"shi","year":"2017","journal-title":"IEEE Trans Smart Grid"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCNC.2018.8390366"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.08.111"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2019.2891704"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2014.2364233"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/en12132521"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/5.192069"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.06.031"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09040610.pdf?arnumber=9040610","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T10:59:54Z","timestamp":1643281194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9040610\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2981817","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}