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Volumn , Issue , 2013, Pages 666-671

Neural networks for wind power generation forecasting: A case study

Author keywords

[No Author keywords available]

Indexed keywords

DATA PREPROCESSING; DATA PREPROCESSING TECHNIQUE; EXTREME LEARNING MACHINE; MULTI LAYER PERCEPTRON; SOUTHERN ITALY; STABLE PERFORMANCE; TRAINING ALGORITHMS; WIND TURBINE POWER;

EID: 84881296501     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNSC.2013.6548818     Document Type: Conference Paper
Times cited : (5)

References (13)
  • 1
    • 0004327559 scopus 로고
    • Evolution of modern wind turbines
    • D. A. Spera, Ed: AMSE Press
    • L. V. Divone, Evolution of modern wind turbines, in Wind Turbine Technology, D. A. Spera, Ed: AMSE Press, 1995, pp. 73138
    • (1995) Wind Turbine Technology , pp. 73138
    • Divone, L.V.1
  • 2
    • 0004196536 scopus 로고
    • A utility perspective of wind energy
    • D. A. Spera, Ed: AMSE Press
    • C. J. Weinberg and D. F. Ancona, A utility perspective of wind energy, in Wind Turbine Technology, D. A. Spera, Ed: AMSE Press, 1995, pp. 589602
    • (1995) Wind Turbine Technology , pp. 589602
    • Weinberg, C.J.1    Ancona, D.F.2
  • 5
    • 84876556058 scopus 로고
    • Stochastic wind prediction for wind turbine system control
    • Oxford, U. K
    • Bossanyi, E. A., 1985, Stochastic Wind Prediction for Wind Turbine System Control, Proc. of 7th British Wind Energy Association Conf. Oxford, U. K., pp. 219226
    • (1985) Proc. of 7th British Wind Energy Association Conf , pp. 219226
    • Bossanyi, E.A.1
  • 6
    • 0030684349 scopus 로고    scopus 로고
    • Using neural networks to predict wind power generation
    • Washington D. C
    • Li, S., O'Hair, E., and Giesselmann, M., 1997, Using neural networks to predict wind power generation, Proc. of Int. Solar Energy Conf. Washington D. C., pp. 415420
    • (1997) Proc. of Int. Solar Energy Conf , pp. 415420
    • Li, S.1    O'hair, E.2    Giesselmann, M.3
  • 7
    • 3543104882 scopus 로고    scopus 로고
    • Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation
    • November
    • L. Shuhui, D. C. Wunsch, E. A. O'Hair, M. G. Giesselmann, Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation, Journal of Solar Energy Engineering, vol. 123, November 2001, pp. 327-332
    • (2001) Journal of Solar Energy Engineering , vol.123 , pp. 327-332
    • Shuhui, L.1    Wunsch, D.C.2    O'hair, E.A.3    Giesselmann, M.G.4
  • 9
    • 80052530027 scopus 로고    scopus 로고
    • Short-term wind power forecasting using ridgelet neural network
    • N. Amjady, F. Keynia, and H. Zareipour Short-term wind power forecasting using ridgelet neural network, Electric Power Systems Research, vol. 81, pp. 2099-2107, 2011
    • (2011) Electric Power Systems Research , vol.81 , pp. 2099-2107
    • Amjady, N.1    Keynia, F.2    Zareipour, H.3
  • 10
    • 33745903481 scopus 로고    scopus 로고
    • Extreme learning machine: Theory and applications
    • G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, Extreme Learning Machine: Theory and applications, Neurocomputing, vol. 70, pp. 489-501, 2006
    • (2006) Neurocomputing , vol.70 , pp. 489-501
    • Huang, G.-B.1    Zhu, Q.-Y.2    Siew, C.-K.3
  • 13
    • 0032028728 scopus 로고    scopus 로고
    • The sample complexity of pattern classification with neural networks: The size of the weights is more important that the size of the network
    • Bartlett, P. L. : The Sample Complexity of Pattern Classification with Neural Networks: the Size of the Weights is More Important that the Size of the Network. IEEE Trans. Inf. Theory 44, n. 2, 525-536 (1998
    • (1998) IEEE Trans. Inf. Theory , vol.44 , Issue.2 , pp. 525-536
    • Bartlett, P.L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.