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Volumn 78, Issue , 2016, Pages 910-915

A hybrid model of EMD and multiple-kernel RVR algorithm for wind speed prediction

Author keywords

EMD; Multiple kernel RVR model; Prediction method; Wind speed

Indexed keywords

ALGORITHMS; FORECASTING; FUNCTIONS; RADIAL BASIS FUNCTION NETWORKS; SPEED; WIND;

EID: 84955512570     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2015.11.116     Document Type: Article
Times cited : (47)

References (11)
  • 1
    • 84897487015 scopus 로고    scopus 로고
    • A methodology for evaluating the spatial variability of wind energy resources: application to assess the potential contribution of wind energy to baseload power
    • F.J. Santos-Alamillos, D. Pozo-Vázquez, J.A. Ruiz-Arias, V. Lara-Fanego, and J. Tovar-Pescador A methodology for evaluating the spatial variability of wind energy resources: application to assess the potential contribution of wind energy to baseload power Renew Energy 69 2014 147 156
    • (2014) Renew Energy , vol.69 , pp. 147-156
    • Santos-Alamillos, F.J.1    Pozo-Vázquez, D.2    Ruiz-Arias, J.A.3    Lara-Fanego, V.4    Tovar-Pescador, J.5
  • 2
    • 84904022801 scopus 로고    scopus 로고
    • An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil
    • Carla Freitas de Andrade, Hely Falcão Maia Neto, Paulo Alexandre Costa Rocha, and Maria Eugênia Vieira da Silva An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil Energy Convers Manage 86 2014 801 808
    • (2014) Energy Convers Manage , vol.86 , pp. 801-808
    • De Andrade, C.F.1    Neto, H.F.M.2    Rocha, P.A.C.3    Da Silva, M.E.V.4
  • 3
    • 84902287844 scopus 로고    scopus 로고
    • Assessment of offshore wind energy potential using mesoscale model and geographic information system
    • Atsushi Yamaguchi, and Takeshi Ishihara Assessment of offshore wind energy potential using mesoscale model and geographic information system Renew Energy 69 2014 506 515
    • (2014) Renew Energy , vol.69 , pp. 506-515
    • Yamaguchi, A.1    Ishihara, T.2
  • 4
    • 84896867030 scopus 로고    scopus 로고
    • Analysis of the short-term overproduction capability of variable speed wind turbines
    • Anca D. Hansen, Müfit Altin, Ioannis D. Margaris, Florin Iov, and Germán C. Tarnowski Analysis of the short-term overproduction capability of variable speed wind turbines Renew Energy 68 2014 326 336
    • (2014) Renew Energy , vol.68 , pp. 326-336
    • Hansen, A.D.1    Altin, M.2    Margaris, I.D.3    Iov, F.4    Tarnowski, G.C.5
  • 5
    • 84899646226 scopus 로고    scopus 로고
    • A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm
    • Zhenhai Guo, Dezhong Chi, Wu Jie, and Wenyu Zhang A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm Energy Convers Manage 84 2014 140 151
    • (2014) Energy Convers Manage , vol.84 , pp. 140-151
    • Guo, Z.1    Chi, D.2    Jie, W.3    Zhang, W.4
  • 6
    • 84937909008 scopus 로고    scopus 로고
    • Wind speed forecasting for wind farms: a method based on support vector regression
    • G. Santamaría-Bonfil, A. Reyes-Ballesteros, and C. Gershenson Wind speed forecasting for wind farms: a method based on support vector regression Renew Energy 85 2016 790 809
    • (2016) Renew Energy , vol.85 , pp. 790-809
    • Santamaría-Bonfil, G.1    Reyes-Ballesteros, A.2    Gershenson, C.3
  • 7
    • 84895929771 scopus 로고    scopus 로고
    • Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission
    • Shahaboddin Shamshirband, Dalibor Petković, Amineh Amini, Nor Badrul Anuar, Vlastimir Nikolić, Žarko Ćojbašić, and et al. Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission Energy 67 2014 623 630
    • (2014) Energy , vol.67 , pp. 623-630
    • Shamshirband, S.1    Petković, D.2    Amini, A.3    Anuar, N.B.4    Nikolić, V.5    Ćojbašić, Ž.6
  • 8
    • 0001224048 scopus 로고    scopus 로고
    • Sparse Bayesian learning and the relevance vector machine
    • Michael E. Tipping Sparse Bayesian learning and the relevance vector machine J Mach Learn Res 1 2001 211 244
    • (2001) J Mach Learn Res , vol.1 , pp. 211-244
    • Tipping, M.E.1
  • 9
    • 70449685716 scopus 로고    scopus 로고
    • Application of the relevance vector machine to canal flow prediction in the Sevier River Basin
    • John Flake, Todd K. Moon, Mac McKee, and Jacob H. Gunther Application of the relevance vector machine to canal flow prediction in the Sevier River Basin Agric Water Manage 97 2 2010 208 214
    • (2010) Agric Water Manage , vol.97 , Issue.2 , pp. 208-214
    • Flake, J.1    Moon, T.K.2    McKee, M.3    Gunther, J.H.4
  • 10
    • 84916201351 scopus 로고    scopus 로고
    • A local and online sifting process for the empirical mode decomposition and its application in aircraft damage detection
    • Seyed Amin Bagherzadeh, and Mehdi Sabzehparvar A local and online sifting process for the empirical mode decomposition and its application in aircraft damage detection Mech Syst Signal Process 54-55 2015 68 83
    • (2015) Mech Syst Signal Process , vol.54-55 , pp. 68-83
    • Bagherzadeh, S.A.1    Sabzehparvar, M.2
  • 11
    • 84907486966 scopus 로고    scopus 로고
    • Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
    • Jaouher Ben Ali, Nader Fnaiech, Lotfi Saidi, Brigitte Chebel-Morello, and Farhat Fnaiech Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals Appl Acoust 89 2015 16 27
    • (2015) Appl Acoust , vol.89 , pp. 16-27
    • Ali, J.B.1    Fnaiech, N.2    Saidi, L.3    Chebel-Morello, B.4    Fnaiech, F.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.