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A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
Abstract
Wind speed forecasting is challenging due to its intermittent nature. The wind speed time series (TS) has nonlinear and nonstationary characteristics and not normally distributed, which make it difficult to be predicted by statistical or computational intelligent methods. Empirical mode decomposition (EMD) and its improved versions are powerful tools to decompose a complex TS into a collection of simpler ones. The improved versions discussed in this paper include ensemble EMD (EEMD), complementary EEMD (CEEMD), and complete EEMD with adaptive noise (CEEMDAN). The EMD and its improved versions are hybridized with two computational intelligence-based predictors: support vector regression (SVR) and artificial neural network (ANN). The EMD-based hybrid forecasting methods are evaluated with 12 wind speed TS. The performances of the hybrid methods are compared and discussed. It shows that EMD and its improved versions enhance the performance of SVR significantly but marginally on ANN, and among the EMD-based hybrid methods, the proposed CEEMDAN-SVR is the best method. Possible future works are also recommended for wind speed forecasting.
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
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Ren, Y., Suganthan, P. N., & Srikanth, N. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.081 · Cited by 435
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Metadata
- Authors
- Ren, Y., Suganthan, P. N., & Srikanth, N.
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2015-01
- Volume
- 6
- Issue
- 1
- Pages
- 236-244
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W2321536237
- Cited by
- 337
- References
- 2
- FWCI
- 10.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ren, Y., Suganthan, P. N., & Srikanth, N. (2015). A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/tste.2014.2365580
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