Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques

Diksha Kaur; Tek Tjing Lie; Nirmal K. C. Nair; Brice Vallès; Diksha Kaur; Tek Tjing Lie; Nirmal K. C. Nair; Brice Vallès

doi:10.3934/energy.2015.1.13

AIMS Energy

2015, Volume 3, Issue 1: 13-24. doi: 10.3934/energy.2015.1.13

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Research article

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

Received: 22 September 2014 Accepted: 03 January 2014 Published: 07 January 2015

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.
- 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[J]. AIMS Energy, 2015, 3(1): 13-24. doi: 10.3934/energy.2015.1.13

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Abstract

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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