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Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques

1 Department of Electrical & Electronic Engineering, Auckland University of Technology, Auckland, New Zealand;
2 Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand;
3 Inland Revenue, Auckland, New Zealand

Special Issues: Wind Power Implementation Challenges

The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT) along with the Auto Regressive Moving Average (ARMA) is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE). A simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ARMA and Artificial Neural Network (ANN)-Ensemble Kalman Filter (EnKF) hybrid technique to verify the effectiveness of the proposed hybrid method. Results of the proposed hybrid show significant improvements in the forecasting error.
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Keywords Wind Speed Forecasting; Wavelet Transform; ARMA; MATLAB

Citation: Diksha Kaur, Tek Tjing Lie, Nirmal K. C. Nair, Brice Vallès. Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques. AIMS Energy, 2015, 3(1): 13-24. doi: 10.3934/energy.2015.1.13


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Copyright Info: 2015, Tek Tjing Lie, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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