{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T21:11:11Z","timestamp":1773090671479,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Projects of Science and Technology Commission of Shanghai Municipality","award":["15JC1401900"],"award-info":[{"award-number":["15JC1401900"]}]},{"name":"Projects of Science and Technology Commission of Shanghai Municipality","award":["No.17511107002"],"award-info":[{"award-number":["No.17511107002"]}]},{"name":"the Open Research Fund of Wanjiang Collaborative Innovation Center for High-end Manufacturing Equipment, Anhui Polytechnic University","award":["GCKJ2018010"],"award-info":[{"award-number":["GCKJ2018010"]}]},{"DOI":"10.13039\/501100013143","name":"National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers","doi-asserted-by":"crossref","award":["61803001"],"award-info":[{"award-number":["61803001"]}],"id":[{"id":"10.13039\/501100013143","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s00500-020-05233-8","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T15:04:53Z","timestamp":1596553493000},"page":"1479-1500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A new compound wind speed forecasting structure combining multi-kernel LSSVM with two-stage decomposition technique"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1333-9180","authenticated-orcid":false,"given":"Sizhou","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingqi","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pinggai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"key":"5233_CR1","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.enconman.2016.05.024","volume":"121","author":"A Aghajani","year":"2016","unstructured":"Aghajani A, Kazemzadeh R, Ebrahimi A (2016) A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm. Energy Convers Manag 121:232\u2013240","journal-title":"Energy Convers Manag"},{"key":"5233_CR2","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1016\/j.apenergy.2011.01.037","volume":"88","author":"H Bouzgou","year":"2011","unstructured":"Bouzgou H, Benoudjit N (2011) Multiple architecture system for wind speed prediction. Appl Energy 88:2463\u20132471","journal-title":"Appl Energy"},{"key":"5233_CR3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.renene.2016.10.030","volume":"102","author":"QL Dong","year":"2017","unstructured":"Dong QL, Sun YH, Li PZ (2017) A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China. Renew Energy 102:241\u2013257","journal-title":"Renew Energy"},{"key":"5233_CR4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.enconman.2017.07.065","volume":"150","author":"P Du","year":"2017","unstructured":"Du P, Wang JZ, Guo ZH et al (2017) Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting. Energy Convers Manag 150:90\u2013107","journal-title":"Energy Convers Manag"},{"key":"5233_CR5","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.apenergy.2017.01.043","volume":"190","author":"C Feng","year":"2017","unstructured":"Feng C, Cui MJ, Hodge B et al (2017) A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl Energy 190:1245\u20131257","journal-title":"Appl Energy"},{"key":"5233_CR6","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.measurement.2019.02.053","volume":"138","author":"T Han","year":"2019","unstructured":"Han T, Liu QN, Zhang L et al (2019) Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD. Measurement 138:400\u2013408","journal-title":"Measurement"},{"key":"5233_CR7","doi-asserted-by":"publisher","first-page":"2253","DOI":"10.1109\/TIA.2013.2262452","volume":"49","author":"AU Haque","year":"2013","unstructured":"Haque AU, Mandal P, Meng J et al (2013) A novel hybrid approach based on wavelet transform and fuzzy ARTMAP networks for predicting wind farm power production. IEEE Trans Ind Appl 49:2253\u20132261","journal-title":"IEEE Trans Ind Appl"},{"key":"5233_CR8","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.rser.2017.07.049","volume":"81","author":"A Hemmati-Sarapardeha","year":"2018","unstructured":"Hemmati-Sarapardeha A, Varameshb A, Huseinc M et al (2018) On the evaluation of the viscosity of nanofluid systems: modeling and data assessment. Renew Sustain Energy Rev 81:313\u2013329","journal-title":"Renew Sustain Energy Rev"},{"key":"5233_CR9","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.energy.2014.12.074","volume":"81","author":"JM Hu","year":"2015","unstructured":"Hu JM, Wang JZ, Ma KL (2015) A hybrid technique for short-term wind speed prediction. Energy 81:563\u2013574","journal-title":"Energy"},{"key":"5233_CR10","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jss.2017.04.016","volume":"137","author":"L Kumar","year":"2018","unstructured":"Kumar L, Krishna Sripada S, Surekac A et al (2018) Effective fault prediction model developed using least square support vector machine (LSSVM)\u201d. J Syst Softw 137:686\u2013712","journal-title":"J Syst Softw"},{"key":"5233_CR11","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.renene.2017.09.089","volume":"116","author":"HM Li","year":"2018","unstructured":"Li HM, Wang JZ, Lu HY et al (2018) Research and application of a combined model based on variable weight for short term wind speed forecasting. Renew Energy 116:669\u2013684","journal-title":"Renew Energy"},{"key":"5233_CR12","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.renene.2013.08.011","volume":"62","author":"D Liu","year":"2014","unstructured":"Liu D, Niu D, Wang H et al (2014) Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 62:592\u2013597","journal-title":"Renew Energy"},{"key":"5233_CR13","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/j.renene.2016.10.074","volume":"103","author":"JQ Liu","year":"2017","unstructured":"Liu JQ, Wang XR, Lu Y (2017) A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system. Renew Energy 103:620\u2013629","journal-title":"Renew Energy"},{"key":"5233_CR14","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1016\/j.asoc.2018.07.027","volume":"71","author":"TH Liu","year":"2018","unstructured":"Liu TH, Wei HK, Zhang KJ (2018) Wind power prediction with missing data using Gaussian process regression and multiple imputation. Appl Soft Comput 71:905\u2013916","journal-title":"Appl Soft Comput"},{"key":"5233_CR15","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.isatra.2016.08.022","volume":"65","author":"M Luo","year":"2016","unstructured":"Luo M, Li CS, Zhang XY et al (2016) Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. ISA Trans 65:556\u2013566","journal-title":"ISA Trans"},{"key":"5233_CR16","unstructured":"Mirjalili S, Hashimx S (2012) A new hybrid PSOGSA algorithm for function optimization. In: International conference on computer and information application, pp 374\u2013377"},{"key":"5233_CR17","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.renene.2017.10.111","volume":"118","author":"J Naik","year":"2018","unstructured":"Naik J, Dash S, Dash PK et al (2018) Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network. Renew Energy 118:180\u2013212","journal-title":"Renew Energy"},{"key":"5233_CR18","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.renene.2014.09.058","volume":"75","author":"GJ Osorio","year":"2015","unstructured":"Osorio GJ, Matias JCO, Catalao JPS (2015) Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew Energy 75:301\u2013307","journal-title":"Renew Energy"},{"key":"5233_CR19","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.enconman.2017.10.021","volume":"153","author":"T Peng","year":"2017","unstructured":"Peng T, Zhou JZ, Zhang C et al (2017) Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine. Energy Convers Manag 153:589\u2013602","journal-title":"Energy Convers Manag"},{"key":"5233_CR20","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi S, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sci 179:2232\u20132248","journal-title":"Inform Sci"},{"key":"5233_CR21","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.petrol.2018.09.085","volume":"173","author":"S Rostami","year":"2019","unstructured":"Rostami S, Rashidi F, Safari H (2019) Prediction of oil-water relative permeability in sandstone and carbonate reservoir rocks using the CSA-LSSVM algorithm. J Petrol Sci Eng 173:170\u2013186","journal-title":"J Petrol Sci Eng"},{"key":"5233_CR22","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.enconman.2014.06.041","volume":"87","author":"S Salcedo-Sanz","year":"2014","unstructured":"Salcedo-Sanz S, Pastor-Sanchez A, Prieto L et al (2014) Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization-extreme learning machine approach. Energy Convers Manag 87:10\u201318","journal-title":"Energy Convers Manag"},{"key":"5233_CR23","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.renene.2015.07.004","volume":"85","author":"G Santamaria-Bonfil","year":"2016","unstructured":"Santamaria-Bonfil G, Reyes-Ballesteros A, Gershenson C (2016) Wind speed forecasting for wind farms: A method based on support vector regression. Renew Energy 85:790\u2013809","journal-title":"Renew Energy"},{"key":"5233_CR24","first-page":"143","volume":"11","author":"JE Stephen","year":"2014","unstructured":"Stephen JE, Kumar SS, Jayakumar J (2014) Nonlinear modeling of a switched reluctance motor using LSSVM-ABC. Acta Polytech Hung 11:143\u2013158","journal-title":"Acta Polytech Hung"},{"key":"5233_CR25","first-page":"1","volume":"40","author":"SZ Sun","year":"2018","unstructured":"Sun SZ, Fu JQ, Zhu F (2018a) A hybrid structure of an extreme learning machine combined with feature selection, signal decomposition and parameter optimization for short-term wind speed forecasting. Trans Inst Meas Control 40:1\u201318","journal-title":"Trans Inst Meas Control"},{"issue":"11","key":"5233_CR26","first-page":"1","volume":"9287097","author":"SZ Sun","year":"2018","unstructured":"Sun SZ, Fu JQ, Zhu F, Xiong N (2018b) A compound structure for wind speed forecasting using MKLSSVM with feature selection and parameter optimization. Math Probl Eng 9287097(11):1\u201321","journal-title":"Math Probl Eng"},{"key":"5233_CR27","doi-asserted-by":"crossref","unstructured":"Sun GP, Jiang CW, Cheng P et al (2018c) Short-term wind power forecasts by a synthetical similar time series data mining method. Renew Energy 115:575\u2013584","DOI":"10.1016\/j.renene.2017.08.071"},{"key":"5233_CR28","first-page":"1","volume":"12334","author":"SZ Sun","year":"2019","unstructured":"Sun SZ, Wei LS, Xu J et al (2019) A new wind power forecasting modeling strategy using two-stage decomposition, feature selection and DAWNN. Energies 12334:1\u201324","journal-title":"Energies"},{"key":"5233_CR29","doi-asserted-by":"crossref","unstructured":"Torres ME, Colominas MA, Schlotthauer G et al (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4144\u20134147","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"5233_CR30","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang JZ, Wei X (2015a) A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China. Energy 91:556\u2013572","DOI":"10.1016\/j.energy.2015.08.039"},{"key":"5233_CR31","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.energy.2015.08.039","volume":"91","author":"Y Wang","year":"2015","unstructured":"Wang Y, Wang JZ, Wei X (2015b) A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China. Energy 91:556\u2013572","journal-title":"Energy"},{"key":"5233_CR32","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.renene.2016.03.103","volume":"94","author":"SX Wang","year":"2016","unstructured":"Wang SX, Zhang N, Wu L et al (2016) Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew Energy 94:629\u201336","journal-title":"Renew Energy"},{"key":"5233_CR33","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1016\/j.renene.2017.06.095","volume":"113","author":"DY Wang","year":"2017","unstructured":"Wang DY, Luo HY, Grunder O et al (2017) Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renew Energy 113:1345\u20131358","journal-title":"Renew Energy"},{"key":"5233_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1793536909000047","volume":"1","author":"Z Wu","year":"2009","unstructured":"Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adaptive Data Anal 1:1\u201341","journal-title":"Adv Adaptive Data Anal"},{"key":"5233_CR35","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jweia.2017.12.024","volume":"174","author":"J Yan","year":"2018","unstructured":"Yan J, Huang GQ, Peng XY et al (2018) A novel wind speed prediction method: hybrid of correlation-aided DWT, LSSVM and GARCH. J Wind Eng Ind Aerod 174:28\u201338","journal-title":"J Wind Eng Ind Aerod"},{"key":"5233_CR36","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.enconman.2017.08.014","volume":"150","author":"H Yin","year":"2017","unstructured":"Yin H, Dong Z, Chen YL et al (2017) An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization. Energy Convers Manag 150:108\u2013121","journal-title":"Energy Convers Manag"},{"key":"5233_CR37","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.enconman.2015.05.065","volume":"101","author":"XH Yuan","year":"2015","unstructured":"Yuan XH, Chen C, Yuan YB et al (2015) Short-term wind power prediction based on LSSVM-GSA model. Energy Convers Manag 101:393\u2013401","journal-title":"Energy Convers Manag"},{"issue":"6","key":"5233_CR38","first-page":"1","volume":"053139","author":"GY Zhang","year":"2014","unstructured":"Zhang GY, Wu YG, Liu YQ (2014) An advanced wind speed multi-step ahead forecasting approach with characteristic component analysis. J Renew Sustain Energy 053139(6):1\u201314","journal-title":"J Renew Sustain Energy"},{"key":"5233_CR39","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.renene.2016.05.023","volume":"96","author":"C Zhang","year":"2016","unstructured":"Zhang C, Wei HK, Zhao JS et al (2016a) Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renew Energy 96:727\u2013737","journal-title":"Renew Energy"},{"key":"5233_CR40","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.renene.2016.05.023","volume":"96","author":"C Zhang","year":"2016","unstructured":"Zhang C, Wei HK, Zhao JS et al (2016b) Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renew Energy 96:727\u2013737","journal-title":"Renew Energy"},{"key":"5233_CR41","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.enconman.2017.04.007","volume":"143","author":"C Zhang","year":"2017","unstructured":"Zhang C, Zhou JZ, Li CS et al (2017) A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting. Energy Convers Manag 143:360\u2013376","journal-title":"Energy Convers Manag"},{"key":"5233_CR42","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1049\/iet-epa.2010.0298","volume":"5","author":"H Zheng","year":"2011","unstructured":"Zheng H, Liao R, Grzybowski S et al (2011) Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimization. IET Electr Power Appl 5:691\u2013696","journal-title":"IET Electr Power Appl"},{"key":"5233_CR43","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.enconman.2017.09.029","volume":"151","author":"WQ Zheng","year":"2017","unstructured":"Zheng WQ, Peng XG, Lu D et al (2017) Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach. Energy Convers Manag 151:737\u2013752","journal-title":"Energy Convers Manag"},{"key":"5233_CR44","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1016\/j.enconman.2010.11.007","volume":"52","author":"JY Zhou","year":"2011","unstructured":"Zhou JY, Shi J, Li G (2011) Fine tuning support vector machines for short-term wind speed forecasting. Energy Convers Manag 52:1990\u20131998","journal-title":"Energy Convers Manag"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05233-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-05233-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05233-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T23:38:52Z","timestamp":1628033932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-05233-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,4]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["5233"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-05233-8","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,4]]},"assertion":[{"value":"4 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"There is no conflict of interest exiting in the submission of this manuscript which has been approved by all authors for publication, and there is no information that might be relevant for the editors and reviewers of soft computing. This study develops a new wind speed forecasting model using two-stage signal decomposition, hybrid PSOGSA algorithm and multi-kernel LSSVM. This article does not contain any studies with human participants performed by any of the authors. This article also does not contain any studies with animals performed by any of the authors. I would like to declare on behalf of my co-authors that the work described is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}