Abstract
In this paper, we select the hierarchical cluster method to classify the wind energy level with the meteorological data, and then apply the 0–1 output method to quantify the wind energy level. Next, we utilize wavelet neural network to fit multivariate wind power data, which solves the problem of randomness, intermittency and volatility of wind power data. Finally, a wind-power numerical experiment shows the ideal fitting results with an error precision of \(1.71\%\) and demonstrates the effectiveness of our model.
X.-Y. Zhang—The research is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2017ZY30) and the second author is supported by Scientific Research Grant-in-Aid from JSPS under grant 15K04987.
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References
Potter, C.W., Negnevitsky, M.: Very short-term wind forecasting for Tasmanian power generation. IEEE Trans. Power Syst. 21, 2 (2006)
Fan, G.F., et al.: Wind power prediction based on artificial neural network. Proc. CSEE 34, 118–123 (2008)
Lin, Y.E., Liu, P.: Combined model based on EMD-SVM for short-term wind power prediction. Proc. CSEE 31, 102–108 (2011)
De Giorgi, M.G., et al.: Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 7, 5251–5272 (2014)
Wang, X.L.: Proposals of amendments of wind power density grade and wind energy area classification. Electr. Power Technol. 19(8) (2010)
Ward, J.J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Zhang, J., et al.: Wavelet neural networks for function learning. IEEE Trans. Signal Process. 43(6), 1485–1497 (1995)
Chen, W.G., Ling, Y., Gan, D.G., Wei, C., Yue, Y.F.: Method to identify developing stages of air-gap discharge in oil-paper insulation based on cluster-wavelet neural network. Power Syst. Technol. 36(7), 126–132 (2012)
Yang, Q., Zhang, J.H., Wang, X.F., Li, W.G.: Wind speed and generated wind power forecast based on wavelet-neural network. Power Syst. Technol. 33(17), 44–48 (2009)
Acknowledgments
The authors thank Beijing Forestry University and Yamagata University. The research is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2017ZY30) and the second author is supported by Scientific Research Grant-in-Aid from JSPS under grant 15K04987.
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Zheng, R., Fang, Q., Liu, Z., Li, B., Zhang, XY. (2017). A Multivariate Wind Power Fitting Model Based on Cluster Wavelet Neural Network. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_10
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DOI: https://doi.org/10.1007/978-981-10-6364-0_10
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