{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:51:56Z","timestamp":1766087516575,"version":"3.37.3"},"reference-count":31,"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":[{"name":"Hangzhou Zhonhen Electric Company, Ltd."},{"name":"Hangzhou Zhonhen Power Cloud Technology Company"},{"name":"Hangzhou Zhonhen Power Energy Company, Ltd."},{"name":"National Kay Research and Development Program of China","award":["2017YFB0403500"],"award-info":[{"award-number":["2017YFB0403500"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LR17F030005"],"award-info":[{"award-number":["LR17F030005"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803136","51677047"],"award-info":[{"award-number":["61803136","51677047"]}],"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.2020.2968101","type":"journal-article","created":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T20:43:47Z","timestamp":1579553027000},"page":"19236-19247","source":"Crossref","is-referenced-by-count":22,"title":["Maximum Power Demand Prediction Using Fbprophet With Adaptive Kalman Filtering"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4615-4449","authenticated-orcid":false,"given":"Chen","family":"Guo","sequence":"first","affiliation":[]},{"given":"Quanbo","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3209-3761","authenticated-orcid":false,"given":"Gang","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Hua","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref31","first-page":"53","article-title":"Choice of the agreed maximum demand in the two part tariff","volume":"14","author":"chen","year":"2012","journal-title":"Power Demand Side Manage"},{"key":"ref30","first-page":"1","article-title":"Research on the theory of observable degree of nonlinear systems and its application","author":"zhuo","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001415590053"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.09.059"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICPEICES.2016.7853603"},{"key":"ref13","first-page":"64","article-title":"Power network load forecasting based on improved BP neural network","volume":"39","author":"zhu","year":"2016","journal-title":"Modern Electronics Technique"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2555788"},{"key":"ref15","first-page":"22","article-title":"Power load forecasting in the time series analysis method based on lifting wavelet","volume":"29","author":"fan","year":"2017","journal-title":"Elect Automat"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2017.1380080"},{"key":"ref17","first-page":"182","article-title":"Trend forecasting of the total number of vehicles on automobile proving ground","volume":"8","author":"zeng","year":"2018","journal-title":"Chin J Automot Eng"},{"key":"ref18","first-page":"4190","article-title":"Artificial intelligence algorithm of optimal time series data model","volume":"32","author":"li","year":"2011","journal-title":"Eng Comput"},{"key":"ref19","first-page":"199","article-title":"Scrapy based GitHub data crawler","volume":"6","author":"zhao","year":"2014","journal-title":"Electronic Technology and Software Engineering"},{"key":"ref28","first-page":"70","article-title":"Analysis and prediction of load characteristics of Beijing-Tianjin-tang power grid","volume":"47","author":"fan","year":"2014","journal-title":"Electronics& Power"},{"key":"ref4","first-page":"126","article-title":"Correlation analysis between maintenance plan and maximum demand load","volume":"23","author":"liu","year":"2016","journal-title":"Technology and Market"},{"article-title":"Short term load forecasting model research based on load characteristic analysis","year":"2013","author":"gao","key":"ref27"},{"key":"ref3","first-page":"1","article-title":"Study on the analysis of influencing factors and forecasting model of the maximum demand for electricity consumption of large industrial consumer","author":"fan","year":"2018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2363584"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/976\/1\/012010"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2789297"},{"key":"ref8","first-page":"1","article-title":"New technologies of load characteristic analysys and load forecasting under the smart grid","author":"li","year":"2014"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MPS.2017.7974410"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.01.034"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CCDC.2018.8407383"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2016.01.035"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2007.12.002"},{"key":"ref22","first-page":"327","article-title":"Design of adaptive cubature Kalman filter based on maximum a posteriori estimation","volume":"29","author":"ding","year":"2014","journal-title":"Control Decis"},{"key":"ref21","first-page":"1","article-title":"Research on fault diagnose of power transformer based on entropy weight and grey relational analysis","author":"zhang","year":"2016"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.083"},{"key":"ref23","first-page":"181","article-title":"An AICKF algorithm with forgetting factor","volume":"35","author":"dai","year":"2019","journal-title":"Bulletin of Science and Technology"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2774771"},{"key":"ref25","first-page":"2","article-title":"Adaptive square CKF method for target tracking based on Sage-Husa algorithm","volume":"36","author":"li","year":"2014","journal-title":"Syst Eng Electron"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/08963903.pdf?arnumber=8963903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:56:09Z","timestamp":1642002969000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8963903\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2968101","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}