메뉴 건너뛰기




Volumn 59, Issue 11, 2014, Pages 1167-1175

An optimized short-term wind power interval prediction method considering nwp accuracy

Author keywords

Genetic algorithm; NWP accuracy; Particle swarm optimization; Relevance vector machine; Wind power interval prediction

Indexed keywords


EID: 84919417492     PISSN: 10016538     EISSN: 18619541     Source Type: Journal    
DOI: 10.1007/s11434-014-0119-7     Document Type: Article
Times cited : (17)

References (24)
  • 1
    • 67349211771 scopus 로고    scopus 로고
    • Forecasting the wind generation using a two-stage network based on meteo-rological information
    • Taylor JW, McSharry PE, Yokyama R et al (2009) Forecasting the wind generation using a two-stage network based on meteo-rological information. IEEE Trans Energy Convers 24:474–482
    • (2009) IEEE Trans Energy Convers , vol.24 , pp. 474-482
    • Taylor, J.W.1    McSharry, P.E.2    Yokyama, R.3
  • 2
    • 76849086905 scopus 로고    scopus 로고
    • Probabilistic load flow with correlated wind power injections
    • Usaola J (2010) Probabilistic load flow with correlated wind power injections. Electr Power Syst Res 80:528–536
    • (2010) Electr Power Syst Res , vol.80 , pp. 528-536
    • Usaola, J.1
  • 3
    • 38349108557 scopus 로고    scopus 로고
    • Analysis of wind power gener-ation and prediction using ANN: A case study
    • Mabel MC, Fernandez E (2008) Analysis of wind power gener-ation and prediction using ANN: a case study. Renew Energy 33:986–992
    • (2008) Renew Energy , vol.33 , pp. 986-992
    • Mabel, M.C.1    Fernandez, E.2
  • 4
    • 51849142610 scopus 로고    scopus 로고
    • Short term wind speed forecasting in La Venta, Oaxaca, Me´xico, using artificial neural networks
    • Cadenas E, Rivera W (2009) Short term wind speed forecasting in La Venta, Oaxaca, Me´xico, using artificial neural networks. Renew Energy 34:274–278
    • (2009) Renew Energy , vol.34 , pp. 274-278
    • Cadenas, E.1    Rivera, W.2
  • 5
    • 60949099322 scopus 로고    scopus 로고
    • Comparison of two new short-term wind-power fore-casting systems
    • Ramirez-Rosado IJ, Fernandez-Jimenez LA, Monteiro Cet al (2009) Comparison of two new short-term wind-power fore-casting systems. Renew Energy 34:1848–1854
    • (2009) Renew Energy , vol.34 , pp. 1848-1854
    • Ramirez-Rosado, I.J.1    Fernandez-Jimenez, L.A.2    Al, M.C.3
  • 6
    • 78649450621 scopus 로고    scopus 로고
    • Short-term wind power forecasting in Portugal by neural networks and wavelet transform
    • Catala˜o JPS, Pousinho HMI, Mendes VMF (2011) Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renew Energy 36:1245–1251
    • (2011) Renew Energy , vol.36 , pp. 1245-1251
    • Jps, C.1    Pousinho, H.2    Mendes, V.3
  • 7
    • 77958124770 scopus 로고    scopus 로고
    • Piecewise support vector machine model for short-term wind-power prediction
    • Liu YQ, Shi J, Yang YP et al (2009) Piecewise support vector machine model for short-term wind-power prediction. Int J Green Energy 6:479–489
    • (2009) Int J Green Energy , vol.6 , pp. 479-489
    • Liu, Y.Q.1    Shi, J.2    Yang, Y.P.3
  • 8
    • 0442296729 scopus 로고    scopus 로고
    • Support vector machines for wind speed prediction
    • Mohandes MA, Halawani TO, Rehman S et al (2004) Support vector machines for wind speed prediction. Renew Energy 29: 939–947
    • (2004) Renew Energy , vol.29 , pp. 939-947
    • Mohandes, M.A.1    Halawani, T.O.2    Rehman, S.3
  • 9
    • 79952454042 scopus 로고    scopus 로고
    • Multiple architecture system for wind speed prediction
    • Bouzgou H, Benoudjit N (2011) Multiple architecture system for wind speed prediction. Appl Energy 88:2463–2471
    • (2011) Appl Energy , vol.88 , pp. 2463-2471
    • Bouzgou, H.1    Benoudjit, N.2
  • 10
    • 80052530027 scopus 로고    scopus 로고
    • Short-term wind power forecasting using ridgelet neural network
    • Amjady N, Keynia F, Zareipour H (2011) Short-term wind power forecasting using ridgelet neural network. Electr Power Syst Res 81:2099–2107
    • (2011) Electr Power Syst Res , vol.81 , pp. 2099-2107
    • Amjady, N.1    Keynia, F.2    Zareipour, H.3
  • 11
    • 0001224048 scopus 로고    scopus 로고
    • Sparse Bayesian learning and the relevance vector machine
    • Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244
    • (2001) J Mach Learn Res , vol.1 , pp. 211-244
    • Tipping, M.E.1
  • 12
    • 21244456503 scopus 로고    scopus 로고
    • Comparison of the support vector machine and relevant vector machine in regression and classifi-cation problems
    • Kunming, China, 6–9 Dec 2004
    • Yu WM, Du TH, Lim K (2004) Comparison of the support vector machine and relevant vector machine in regression and classifi-cation problems. In: 8th International conference on control, automation, robotics and vision, vol 2. Kunming, China, 6–9 Dec 2004, pp 1309–1314
    • (2004) 8Th International Conference on Control, Automation, Robotics and Vision, Vol 2 , pp. 1309-1314
    • Yu, W.M.1    Du, T.H.2    Lim, K.3
  • 13
    • 18244383805 scopus 로고    scopus 로고
    • Relevance vector machine and support vector machine classifier analysis of scan-ning laser polarimetry retinal nerve fiber layer measurements
    • Bowd C, Medeiros FA, Zhang ZB et al (2005) Relevance vector machine and support vector machine classifier analysis of scan-ning laser polarimetry retinal nerve fiber layer measurements. Invest Ophthalmol Vis Sci 46:1322–1329
    • (2005) Invest Ophthalmol Vis Sci , vol.46 , pp. 1322-1329
    • Bowd, C.1    Medeiros, F.A.2    Zhang, Z.B.3
  • 14
    • 58749101018 scopus 로고    scopus 로고
    • Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
    • Widodo A, Kim EY, Son JD et al (2009) Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Syst Appl 36:7252–7261
    • (2009) Expert Syst Appl , vol.36 , pp. 7252-7261
    • Widodo, A.1    Kim, E.Y.2    Son, J.D.3
  • 16
    • 77956366904 scopus 로고    scopus 로고
    • Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting
    • Duan Q, Zhao JG, Ma Y (2010) Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting. Electr Mach Control 14:34–38
    • (2010) Electr Mach Control , vol.14 , pp. 34-38
    • Duan, Q.1    Zhao, J.G.2    Ma, Y.3
  • 17
    • 77949658813 scopus 로고    scopus 로고
    • Application of rel-evance vector machine and logistic regression for machine deg-radation assessment
    • Caesarendra W, Widodo A, Yang BS (2010) Application of rel-evance vector machine and logistic regression for machine deg-radation assessment. Mech Syst Signal Proc 24:1161–1171
    • (2010) Mech Syst Signal Proc , vol.24 , pp. 1161-1171
    • Caesarendra, W.1    Widodo, A.2    Yang, B.S.3
  • 19
    • 2942570109 scopus 로고    scopus 로고
    • A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation
    • Ioannis G, Minas C, John B et al (2004) A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans Energy Convers 19:352–361
    • (2004) IEEE Trans Energy Convers , vol.19 , pp. 352-361
    • Ioannis, G.1    Minas, C.2    John, B.3
  • 20
    • 84876523900 scopus 로고    scopus 로고
    • An RBF neural network combined with OLS algorithm and genetic algorithm for short-term wind power forecasting
    • Chang WY (2013) An RBF neural network combined with OLS algorithm and genetic algorithm for short-term wind power forecasting. J Appl Math 2013:1–9
    • (2013) J Appl Math , vol.2013 , pp. 1-9
    • Chang, W.Y.1
  • 21
    • 80051556876 scopus 로고    scopus 로고
    • Very short term wind power forecasting using PSO-neural network hybrid system
    • Pratheepraj E, Abraham A, Deepa SN et al (2011) Very short term wind power forecasting using PSO-neural network hybrid system. Comm Comput Inf Sci 192:503–511
    • (2011) Comm Comput Inf Sci , vol.192 , pp. 503-511
    • Pratheepraj, E.1    Abraham, A.2    Deepa, S.N.3
  • 23
    • 37749024565 scopus 로고    scopus 로고
    • Genetic algorithms theory and appli-cations (in Chinese). National Defence Industry
    • Zhou M, Sun SD (1999) Genetic algorithms theory and appli-cations (in Chinese). National Defence Industry, Beijing
    • (1999) Beijing
    • Zhou, M.1    Sun, S.D.2
  • 24
    • 63349110410 scopus 로고    scopus 로고
    • Parameters selection of support vector regression based on genetic algorithm
    • Li LM, Wen GR (2008) Parameters selection of support vector regression based on genetic algorithm. Comput Eng Appl 44: 23–26
    • (2008) Comput Eng Appl , vol.44 , pp. 23-26
    • Li, L.M.1    Wen, G.R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.