Applying Wavelet Filters in Wind Forecasting Methods

José A. Domínguez-Navarro, Tania B. Lopez-Garcia and Sandra Minerva Valdivia-Bautista
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José A. Domínguez-Navarro: Department of Electrical Engineering, EINA, University of Zaragoza, 50018 Zaragoza, Spain
Tania B. Lopez-Garcia: Department of Electrical Engineering, EINA, University of Zaragoza, 50018 Zaragoza, Spain
Sandra Minerva Valdivia-Bautista: Centro Universitario de Ciencias e Ingenierías (CUCEI), Universidad de Guadalajara (UDG), Guadalajara 44160, Mexico

Energies, 2021, vol. 14, issue 11, 1-22

Abstract: Wind is a physical phenomenon with uncertainties in several temporal scales, in addition, measured wind time series have noise superimposed on them. These time series are the basis for forecasting methods. This paper studied the application of the wavelet transform to three forecasting methods, namely, stochastic, neural network, and fuzzy, and six wavelet families. Wind speed time series were first filtered to eliminate the high-frequency component using wavelet filters and then the different forecasting methods were applied to the filtered time series. All methods showed important improvements when the wavelet filter was applied. It is important to note that the application of the wavelet technique requires a deep study of the time series in order to select the appropriate family and filter level. The best results were obtained with an optimal filtering level and improper selection may significantly affect the accuracy of the results.

Keywords: wavelet transforms; forecasting methods; wind energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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