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The Scientific World Journal
Volume 2014 (2014), Article ID 914127, 12 pages
http://dx.doi.org/10.1155/2014/914127
Research Article

A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China

Received 17 April 2014; Accepted 18 June 2014; Published 21 July 2014

Academic Editor: Adolfo Iulianelli

Copyright © 2014 Jujie Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China’s wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.