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Improved Wind Speed Prediction Using Empirical Mode DecompositionZHANG, Y. , ZHANG, C. , SUN, J. , GUO, J.
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renewable energy, wind speed prediction, empirical mode decomposition, radial basis function neural network, least squares support vector basis
wind(15), prediction(15), energy(10), speed(7), artificial(7), system(5), model(5), forecasting(5), time(4), term(4)
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About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02001
Web of Science Accession Number: 000434245000001
SCOPUS ID: 85047879257
Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD), the Radial Basis Function Neural Network (RBF) and the Least Square Support Vector Machine (SVM), an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM) is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM), the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.
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