Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

Hui Liu, Hong-qi Tian, Di-fu Pan and Yan-fei Li

Applied Energy, 2013, vol. 107, issue C, 208 pages

Abstract: 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-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] 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.

Keywords: Wind speed predictions; Wind speed forecasting; Hybrid model; Signal decomposition; ANN; ARIMA (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (81)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:107:y:2013:i:c:p:191-208

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DOI: 10.1016/j.apenergy.2013.02.002

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