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 HIGH-IMPACT PAPER 

Improved Wind Speed Prediction Using Empirical Mode Decomposition

ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on IEEExplore See more information about ZHANG, Y. on Web of Science, ZHANG, C. See more information about  ZHANG, C. on SCOPUS See more information about  ZHANG, C. on SCOPUS See more information about ZHANG, C. on Web of Science, SUN, J. See more information about  SUN, J. on SCOPUS See more information about  SUN, J. on SCOPUS See more information about SUN, J. on Web of Science, GUO, J. See more information about GUO, J. on SCOPUS See more information about GUO, J. on SCOPUS See more information about GUO, J. on Web of Science
 
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Download PDF pdficon (1,055 KB) | Citation | Downloads: 1,848 | Views: 28,569

Author keywords
renewable energy, wind speed prediction, empirical mode decomposition, radial basis function neural network, least squares support vector basis

References keywords
wind(15), prediction(15), energy(10), speed(7), artificial(7), system(5), model(5), forecasting(5), time(4), term(4)
Blue keywords are present in both the references section and the paper title.

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

Abstract
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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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 1,958 TCR
SCOPUS® Citations for all references: 2,316 TCR

Web of Science® Average Citations per reference: 65 ACR
SCOPUS® Average Citations per reference: 77 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-14 06:52 in 159 seconds.




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