{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T04:02:10Z","timestamp":1750478530274,"version":"3.41.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T00:00:00Z","timestamp":1498694400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000836","name":"University of Liverpool","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000836","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2018,2]]},"DOI":"10.1007\/s12559-017-9486-0","type":"journal-article","created":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T01:21:07Z","timestamp":1498699267000},"page":"105-116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Semi-blind Model with Parameter Identification for Building Temperature Estimation"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6805-7861","authenticated-orcid":false,"given":"Xing","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eng Gee","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,6,29]]},"reference":[{"issue":"2","key":"9486_CR1","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TCE.2014.6851994","volume":"60","author":"J Han","year":"2014","unstructured":"Han J, Choi Cs, Park Wk, Lee I, Kim Sh. Smart home energy management system including renewable energy based on ZigBee and PLC. IEEE Trans Consum Electron 2014;60(2):198\u2013202.","journal-title":"IEEE Trans Consum Electron"},{"issue":"3","key":"9486_CR2","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.enbuild.2007.03.007","volume":"40","author":"L Prez-Lombard","year":"2008","unstructured":"Prez-Lombard L, Ortiz J, Pout C. A review on buildings energy consumption information. Energy Build 2008;40(3):394\u20138.","journal-title":"Energy Build"},{"key":"9486_CR3","doi-asserted-by":"crossref","unstructured":"Wen Y, Burke W. Real-time dynamic house thermal model identification for predicting HVAC energy consumption. In: 2013 IEEE Green Technologies Conference. Denver, Colorado; 2013. p. 367\u201372.","DOI":"10.1109\/GreenTech.2013.63"},{"issue":"3","key":"9486_CR4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MCI.2011.941590","volume":"6","author":"M De Felice","year":"2011","unstructured":"De Felice M, Yao X. Short-term load forecasting with neural network ensembles: a comparative study [application notes]. IEEE Comput Intell Mag 2011;6(3):47\u201356.","journal-title":"IEEE Comput Intell Mag"},{"issue":"3","key":"9486_CR5","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TCST.2013.2272178","volume":"22","author":"F Oldewurtel","year":"2014","unstructured":"Oldewurtel F, Jones CN, Parisio A, Morari M. Stochastic model predictive control for building climate control. IEEE Trans Control Syst Technol 2014;22(3):1198\u2013205.","journal-title":"IEEE Trans Control Syst Technol"},{"issue":"6","key":"9486_CR6","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1109\/TSMCC.2011.2174983","volume":"42","author":"Z Yang","year":"2012","unstructured":"Yang Z, Li X, Bowers CP, Schnier T, Tang K, Yao X. An efficient evolutionary approach to parameter identification in a building thermal model. IEEE Trans Syst, Man, Cybern, Part C Appl Rev 2012;42 (6):957\u201369.","journal-title":"IEEE Trans Syst, Man, Cybern, Part C Appl Rev"},{"key":"9486_CR7","doi-asserted-by":"crossref","unstructured":"Park H, Ruellan M, Bouvet A, Monmasson E, Bennacer R. Thermal parameter identification of simplified building model with electric appliance. In: 2011 11th International Conference on Electrical Power Quality and Utilisation (EPQU). Lisbon, Portugal; 2011. p. 1\u20136.","DOI":"10.1109\/EPQU.2011.6128822"},{"key":"9486_CR8","doi-asserted-by":"crossref","unstructured":"Mallikarjun S, Gautam AR, Muniyasamy K, Maharaja M, Subathra B, Srinivasan S. 2015. Lasso based building thermal model for heating, ventilation and air-conditioning control. In: IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). Coimbatore, India; 2015. p. 1\u20136.","DOI":"10.1109\/ICECCT.2015.7226011"},{"issue":"4","key":"9486_CR9","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1007\/s12559-016-9409-5","volume":"8","author":"L Xu","year":"2016","unstructured":"Xu L, Ding S, Xu X, Zhang N. Self-adaptive extreme learning machine optimized by rough set theory and affinity propagation clustering. Cogn Comput 2016;8(4):720\u20138.","journal-title":"Cogn Comput"},{"issue":"6","key":"9486_CR10","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1007\/s12559-015-9341-0","volume":"7","author":"I Khamassi","year":"2015","unstructured":"Khamassi I, Sayed-Mouchaweh M, Hammami M, Gh\u00e9dira K. Self-adaptive windowing approach for handling complex concept drift. Cogn Comput 2015;7(6):772\u20130.","journal-title":"Cogn Comput"},{"issue":"2","key":"9486_CR11","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1007\/s12559-012-9191-y","volume":"5","author":"M Boaro","year":"2013","unstructured":"Boaro M, Fuselli D, Angelis FD, Liu D, Wei Q, Piazza F. Adaptive dynamic programming algorithm for renewable energy scheduling and battery management. Cogn Comput 2013;5(2):264\u201377.","journal-title":"Cogn Comput"},{"issue":"1","key":"9486_CR12","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s11771-014-1940-5","volume":"21","author":"Y Gao","year":"2014","unstructured":"Gao Y, Fan R, Zhang Q-L, Roux JJ. Building dynamic thermal simulation of low-order multi-dimensional heat transfer. J Central South Univ 2014;21(1):293\u2013302.","journal-title":"J Central South Univ"},{"issue":"3","key":"9486_CR13","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/TCST.2011.2124461","volume":"20","author":"Y Ma","year":"2012","unstructured":"Ma Y, Borrelli F, Hencey B, Coffey B, Bengea S, Haves P. Model predictive control for the operation of building cooling systems. IEEE Trans Control Syst Technol 2012;20(3):796\u2013803.","journal-title":"IEEE Trans Control Syst Technol"},{"key":"9486_CR14","doi-asserted-by":"crossref","unstructured":"Skruch P. A general fractional-order thermal model for buildings and its properties. Heidelberg: Springer International Publishing; 2013, p. 213\u2013220.","DOI":"10.1007\/978-3-319-00933-9_19"},{"issue":"1","key":"9486_CR15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s12273-015-0263-2","volume":"9","author":"M Qin","year":"2016","unstructured":"Qin M, Yang J. Evaluation of different thermal models in EnergyPlus for calculating moisture effects on building energy consumption in different climate conditions. Build Simul 2016;9(1):15\u201325.","journal-title":"Build Simul"},{"key":"9486_CR16","doi-asserted-by":"crossref","unstructured":"Xiong G, Kundu A, Kundu A, Fisher TS. Thermal modeling of supercapacitors. Cham: Springer International Publishing; 2015, p. 115\u2013141.","DOI":"10.1007\/978-3-319-20242-6"},{"issue":"2","key":"9486_CR17","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TASE.2014.2356337","volume":"13","author":"SA Vaghefi","year":"2016","unstructured":"Vaghefi SA, Jafari MA, Zhu J, Brouwer J, Lu Y. A hybrid physics-based and data driven approach to optimal control of building cooling\/heating systems. IEEE Trans Autom Sci Eng 2016;13(2):600\u201310.","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"3","key":"9486_CR18","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/LCOMM.2015.2390648","volume":"19","author":"Y Hashemi","year":"2015","unstructured":"Hashemi Y, Banihashemi AH. On characterization and efficient exhaustive search of elementary trapping sets of variable-regular LDPC codes. IEEE Commun Lett 2015;19(3):323\u201326.","journal-title":"IEEE Commun Lett"},{"issue":"5","key":"9486_CR19","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1109\/LCOMM.2016.2539255","volume":"20","author":"AT Abebe","year":"2016","unstructured":"Abebe AT, Kang CG. Iterative order recursive least square estimation for exploiting frame-wise sparsity in compressive sensing-based MTC. IEEE Commun Lett 2016;20(5):1018\u201321.","journal-title":"IEEE Commun Lett"},{"issue":"1","key":"9486_CR20","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1109\/TPWRD.2015.2448640","volume":"31","author":"P Yutthagowith","year":"2016","unstructured":"Yutthagowith P, Pattanadech N. Improved least-square prony analysis technique for parameter evaluation of lightning impulsevoltage and current. IEEE Trans Power Deliv 2016;31(1):271\u201377.","journal-title":"IEEE Trans Power Deliv"},{"key":"9486_CR21","doi-asserted-by":"crossref","unstructured":"Guilin T, Yunming Q. Improved least square method apply in ship performance analysis. In: Proceedings 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE). Chengdu, China; 2010. vol. 5, p. 594\u2013596.","DOI":"10.1109\/ICACTE.2010.5579420"},{"issue":"4","key":"9486_CR22","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/TSP.2015.2498136","volume":"64","author":"JJ Jeong","year":"2016","unstructured":"Jeong JJ, Kim SH, Koo G, Kim SW. Mean-square deviation analysis of multiband-structured subband adaptive filter algorithm. IEEE Trans Signal Process 2016;64(4):985\u201394.","journal-title":"IEEE Trans Signal Process"},{"key":"9486_CR23","doi-asserted-by":"crossref","unstructured":"Liu Y. Mean square error of survey estimates. Netherlands, Dordrecht: Springer; 2014. p. 3892\u20133893.","DOI":"10.1007\/978-94-007-0753-5_1754"},{"issue":"2","key":"9486_CR24","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1049\/iet-cvi.2015.0146","volume":"10","author":"NA Azis","year":"2016","unstructured":"Azis NA, Jeong YS, Choi HJ, Iraqi Y. Weighted averaging fusion for multi-view skeletal data and its application in action recognition. IET Comput Vis 2016;10(2):134\u201342.","journal-title":"IET Comput Vis"},{"key":"9486_CR25","doi-asserted-by":"crossref","unstructured":"Bonfietti A, Lombardi M. The weighted average constraint. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 191\u2013206.","DOI":"10.1007\/978-3-642-33558-7_16"},{"issue":"3","key":"9486_CR26","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/TSMC.2015.2426133","volume":"46","author":"L Wu","year":"2016","unstructured":"Wu L, Liu S, Yang Y. A gray model with a time varying weighted generating operator. IEEE Trans Syst, Man, Cybern Syst 2016;46(3):427\u201333.","journal-title":"IEEE Trans Syst, Man, Cybern Syst"},{"key":"9486_CR27","doi-asserted-by":"crossref","unstructured":"Gao X, Huang T, Wang Z, Xiao M. Exploiting a modified gray model in back propagation neural networks for enhanced forecasting. Cogn Comput. 2014;6.","DOI":"10.1007\/s12559-014-9247-2"},{"issue":"1","key":"9486_CR28","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/JSEE.2015.00015","volume":"26","author":"B Zeng","year":"2015","unstructured":"Zeng B, Li C, Chen G, Long X. Equivalency and unbiasedness of grey prediction models. J Syst Eng Electron 2015;26(1):110\u2013 118.","journal-title":"J Syst Eng Electron"},{"issue":"1","key":"9486_CR29","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/JSEE.2015.00013","volume":"26","author":"N Xie","year":"2015","unstructured":"Xie N, Liu S. Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model. J Syst Eng Electron 2015;26(1):96\u2013102.","journal-title":"J Syst Eng Electron"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-017-9486-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-017-9486-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-017-9486-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T07:52:29Z","timestamp":1750405949000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-017-9486-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,29]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,2]]}},"alternative-id":["9486"],"URL":"https:\/\/doi.org\/10.1007\/s12559-017-9486-0","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2017,6,29]]}}}