{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:19:51Z","timestamp":1764937191195,"version":"3.37.3"},"reference-count":48,"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":"Young Scientists Fund of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51806193"],"award-info":[{"award-number":["51806193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M630672"],"award-info":[{"award-number":["2018M630672"]}],"id":[{"id":"10.13039\/501100002858","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.2020.3017655","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T20:29:23Z","timestamp":1597782563000},"page":"158928-158940","source":"Crossref","is-referenced-by-count":15,"title":["Industrial Ultra-Short-Term Load Forecasting With Data Completion"],"prefix":"10.1109","volume":"8","author":[{"given":"Haoyu","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Angjian","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5884-1940","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peizhe","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Yao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"A satellite clock prediction strategy based on grey neural network","author":"yanghai","year":"2019","journal-title":"Surveying and Mapping"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1109\/TAC.2009.2019800"},{"key":"ref33","first-page":"36","article-title":"The clonal selection algorithm with engineering applications","author":"de castro","year":"2000","journal-title":"Proc GECCO Workshop Artificial Immune Syst"},{"key":"ref32","first-page":"99","article-title":"Analysis of short-term load problems of power systems based on time series","author":"zhou","year":"2017","journal-title":"Power Column"},{"key":"ref31","first-page":"70","article-title":"Research on sales forecast of North China power grid based on seasonal ARIMA model","volume":"37","author":"herui","year":"2009","journal-title":"East China Electric Power"},{"key":"ref30","first-page":"969","article-title":"A method for identifying the axis trajectory of aeroengine based on support vector machine decision tree","author":"liuhai","year":"2019","journal-title":"Chinese Journal of Construction Machinery"},{"year":"2018","author":"yongle","journal-title":"Nonlinear fault diagnosis based on Kalman filter","key":"ref37"},{"doi-asserted-by":"publisher","key":"ref36","DOI":"10.1007\/11758501_92"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1007\/s00500-006-0049-7"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1007\/978-3-540-30549-1_126"},{"key":"ref10","first-page":"154","article-title":"Short-term electric load forecasting of extreme learning machine based on particle swarm optimization","author":"jie","year":"2019","journal-title":"J Des Manuf Autom"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1057\/s41274-016-0130-2"},{"key":"ref11","first-page":"42","article-title":"Short-term load forecasting based on improved Kalman filter algorithm","author":"xin","year":"2019"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1016\/j.jweia.2008.03.013"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1109\/SGCF.2018.8408964"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/EIConRus.2019.8656796"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/CCDC.2017.7978658"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1016\/j.rser.2014.12.012"},{"key":"ref17","article-title":"Research on component data completion based on machine learning","volume":"7","author":"xueyun","year":"2019","journal-title":"J Math Learn Res"},{"key":"ref18","first-page":"155","article-title":"A comparison study of missing value processing methods","volume":"31","author":"liu","year":"2004","journal-title":"Comput Sci"},{"key":"ref19","article-title":"Learning internal representation by error propagation, parallel distributed processing","volume":"1","author":"rumelhart","year":"1986"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/APPEEC.2015.7380872"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1016\/j.rser.2013.12.054"},{"year":"2014","author":"wenlong","article-title":"Short-term load forecasting based on least squares support vector machine","key":"ref27"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1109\/TPWRS.2013.2287871"},{"key":"ref6","first-page":"29","article-title":"Analysis of Yunnan industrial load electricity characteristics","author":"xiuzhen","year":"2015","journal-title":"Yunnan Power Technology"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.3390\/en7085251","article-title":"Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN)","volume":"7","author":"de giorgi","year":"2014","journal-title":"Energies"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1016\/j.rser.2015.04.148"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/TPWRS.2013.2249596"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.4236\/jpee.2014.24023"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/NAPS.2010.5619586"},{"key":"ref9","first-page":"86","article-title":"Short-term load forecasting of chaotic optimization PSO-LSSVM algorithm","author":"xiaohong","year":"2019","journal-title":"J Lanzhou Univ Tech"},{"key":"ref1","first-page":"20","article-title":"Application of power engineering technology in smart grid construction","author":"yuying","year":"2019","journal-title":"Power Electron"},{"doi-asserted-by":"publisher","key":"ref46","DOI":"10.1016\/j.asoc.2017.03.016"},{"year":"1975","author":"holland","article-title":"Genetic algorithms, computer programs that evolve in ways that even their creators do not fully understand","key":"ref20"},{"doi-asserted-by":"publisher","key":"ref45","DOI":"10.1016\/j.enconman.2018.06.098"},{"year":"2017","author":"hubara","article-title":"Binarized back-propagation: Training binarized neural networks with binarized gradients","key":"ref48"},{"year":"2014","author":"liang","article-title":"The data preprocessing analysis and research based on SVR","key":"ref22"},{"year":"2018","author":"hubara","article-title":"Quantized back-propagation: Training binarized neural networks with quantized gradients","key":"ref47"},{"key":"ref21","first-page":"18","article-title":"An improved genetic algorithm for optimizing automobile fault diagnosis neural network","volume":"1","author":"su","year":"2011","journal-title":"J Ludong Univ"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1109\/CompComm.2017.8322885"},{"key":"ref24","article-title":"A genetic algorithm for dynamic mutation probability","author":"kai","year":"2019","journal-title":"Inf Technol"},{"key":"ref41","first-page":"1205","article-title":"Vehicle dynamic weighing data processing based on grey neural network","volume":"29","author":"tan","year":"2016","journal-title":"Chin J Sens Actuators"},{"year":"2018","author":"longlong","article-title":"Short-term electric load forecasting based on SARIMA and SVR","key":"ref23"},{"year":"2012","author":"liang","journal-title":"Recommended System Practice","key":"ref44"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/ICNN.1995.488968"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1109\/CCDC.2015.7161895"},{"key":"ref25","first-page":"5","article-title":"Short-term power load forecasting based on improved PSO-LSSVM","volume":"35","author":"ma","year":"2016","journal-title":"Techn Autom Appl"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09170589.pdf?arnumber=9170589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:34:00Z","timestamp":1643146440000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9170589\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3017655","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}