{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:15:56Z","timestamp":1771524956628,"version":"3.50.1"},"reference-count":71,"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-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072363"],"award-info":[{"award-number":["62072363"]}],"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.2020.3044052","type":"journal-article","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T21:21:11Z","timestamp":1607721671000},"page":"222824-222840","source":"Crossref","is-referenced-by-count":13,"title":["A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5499-9217","authenticated-orcid":false,"given":"Huibin","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4043-4463","authenticated-orcid":false,"given":"Bilin","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6058-4832","authenticated-orcid":false,"given":"Genqing","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6275-8808","authenticated-orcid":false,"given":"Xiaojun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2019.106191"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.3390\/su11051272"},{"key":"ref39","first-page":"37","article-title":"Short-term load prediction of natural gas in winter in Xi&#x2019;an city","volume":"29","author":"liu","year":"2009","journal-title":"Gas & Heat"},{"key":"ref38","first-page":"79","article-title":"Application of time series analysis to load forecast of natural gas in town","volume":"4","author":"jiao","year":"2001","journal-title":"J Harbin Univ Civil Eng Archit"},{"key":"ref33","first-page":"92","article-title":"&#x2018;Time series model of predicting short period cit&#x2019; gas load","volume":"22","author":"jiao","year":"2002","journal-title":"Natural Gas Industry"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1360\/N092014-00202"},{"key":"ref31","first-page":"77","article-title":"Hourly load prediction for natural gas based on haar wavelet transforming and ARIMA-RBF","volume":"4","author":"qiao","year":"2015","journal-title":"Journal of Petrochemical Universities"},{"key":"ref30","first-page":"118","article-title":"A forecasting model of natural gas daily load based on wavelet transform and LSSVM-DE","volume":"34","author":"qiao","year":"2014","journal-title":"Natural Gas Industry"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TAPENERGY.2015.7229635"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2016.2634625"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref34","first-page":"155","article-title":"Study on forecasting system of city gas load","volume":"1","author":"jiao","year":"2005","journal-title":"Natural Gas Industry"},{"key":"ref60","first-page":"6409","article-title":"Short-term load forecasting method based on outlier robust extreme learning machine considering adaptive load detection and repair","volume":"36","author":"peng","year":"2016","journal-title":"Proc CSEE"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00677-w"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-019-00265-3"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2016.2558563"},{"key":"ref28","first-page":"87","article-title":"Modelling and simulation of elman neural network on short&#x2014;Term gas load prediction","volume":"29","author":"song","year":"2016","journal-title":"Ind Control Comput"},{"key":"ref64","first-page":"155","article-title":"Improved evaluation index based short-term interval prediction of fluctuation Load","volume":"44","author":"xu","year":"2020","journal-title":"Autom Electr Power Syst"},{"key":"ref27","first-page":"51","article-title":"Gas load forecasting method based on integrated deep learning algorithms","volume":"28","author":"wang","year":"2019","journal-title":"Comput Syst Appl"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2018.02.002"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1080\/15325000290085370"},{"key":"ref29","author":"song","year":"2019","journal-title":"Short-Term Gas Load Forecasting Based on BP Neural Network and Multivariable Linear Regression"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/IYCE.2017.8003734"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.2478\/jlst-2020-0004"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105548"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2018.10.002"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2019.111068"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2498166"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2015.2390632"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2015.2511140"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.10.108"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2019.106584"},{"key":"ref26","first-page":"305","article-title":"Short-term gas forecasting based on EMD and PSO-WNN","volume":"33","author":"zhang","year":"2016","journal-title":"Comput Simul"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2272-1"},{"key":"ref50","first-page":"82","article-title":"Parameter optimization for BP neural network with GA on short&#x2014;Term gas load prediction","volume":"25","author":"song","year":"2012","journal-title":"Ind Control Comput"},{"key":"ref51","article-title":"Research on forecasting methods of natural gas load based on particle swarm optimization-least squares support vector machine","author":"wang","year":"0","journal-title":"J East China Univ Sci Technol"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.3390\/en11082008"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3390\/en12020218"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1177\/1687814017711081"},{"key":"ref56","first-page":"175","article-title":"Short-term natural gas load forecasting based on wavelet neural network optimized by genetic algorithm","volume":"29","author":"liu","year":"2020","journal-title":"Comput Syst Appl"},{"key":"ref55","first-page":"29","article-title":"Gas daily load forecasting based on fruit fly optimization algorithm and SVM","volume":"24","author":"song","year":"2017","journal-title":"Control Eng China"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2019.06.003"},{"key":"ref53","first-page":"42","article-title":"Analysis of relationship between natural gas load and air temperature in heating season in Hebei Province","volume":"48","author":"duan","year":"2019","journal-title":"Chemical Engineering of Oil and Gas"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106332"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/EEM.2016.7521227"},{"key":"ref11","first-page":"29","article-title":"Short-term natural gas consumption forecasting","author":"poto?nik","year":"2007","journal-title":"Proceedings of the IASTED Int Conference -Applied Modeling & Simulation"},{"key":"ref40","first-page":"39","article-title":"Gas load prediction based on regression analysis","volume":"38","author":"liu","year":"2012","journal-title":"Inner Mongolia Petrochemical Industry"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2007.03.001"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.10.175"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-019-0548-y"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5545\/sv-jme.2015.2548"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20282-7_28"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.116905"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119386"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.109856"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117283"},{"key":"ref3","year":"2019","journal-title":"China Statistical Yearbook 2019"},{"key":"ref6","first-page":"490","article-title":"Gas load and progression of research works","volume":"22","author":"yan","year":"2002","journal-title":"Gas & Heat"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2873696"},{"key":"ref8","first-page":"58","article-title":"Overview of the world Natural gas Industry in 2000","volume":"5","author":"zhou","year":"2001","journal-title":"Natural Gas Economy"},{"key":"ref7","author":"bobrovski","year":"2008","journal-title":"Natural Gas Pipeline Transportation"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1260\/014459808787548705"},{"key":"ref9","year":"2020","journal-title":"When Will China&#x2019;s Natural Gas Production Peak Come"},{"key":"ref46","first-page":"77","article-title":"Short-term and medium-term gas demand load forecasting by neural networks","volume":"31","author":"azari","year":"2012","journal-title":"Iran J Chem Chem Eng"},{"key":"ref45","first-page":"191","article-title":"Combination of neural networks forecasters for monthly natural gas consumption prediction","volume":"19","author":"kizilaslan","year":"2009","journal-title":"Neural Netw World"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.04.102"},{"key":"ref47","first-page":"3591","article-title":"Research on natural gas load forecasting based on support vector regression","author":"liu","year":"2004","journal-title":"Proc World Congr Intell Contr Automat"},{"key":"ref42","first-page":"127","article-title":"Urban gas load forecasting based on grey theory","volume":"4","author":"wang","year":"2014","journal-title":"Public Communication of Science & Technology"},{"key":"ref41","first-page":"26","article-title":"Forecast of long-term gas load in small town based on grey theory","volume":"14","author":"wang","year":"2007","journal-title":"J Central South Univ Technol"},{"key":"ref44","first-page":"331","article-title":"Application of neural-network in natural gas load forecasting","volume":"23","author":"zhao","year":"2003","journal-title":"Gas & Heat"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/72.839015"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09291485.pdf?arnumber=9291485","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:12Z","timestamp":1639771032000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9291485\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3044052","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}