期刊
APPLIED ENERGY
卷 107, 期 -, 页码 191-208出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2013.02.002
关键词
Wind speed predictions; Wind speed forecasting; Hybrid model; Signal decomposition; ANN; ARIMA
资金
- Fundamental Research Funds for the Central Universities of China [2012QNZT029]
- National Natural Science Foundation of China [U1134203]
Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BEGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BEGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them. (C) 2013 Elsevier Ltd. All rights reserved.
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