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- Title
Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study.
- Authors
Chuanan Yao; Xiankun Gao; Yongchang Yu
- Abstract
Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy inmany countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of windspeed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term dforecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN) and loose wavelet neural network (LWNN) in this study, and the third model is a new hybridmethod based on the CWNNand LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed fromtwo test stations inNorth China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.
- Subjects
WIND speed; ARTIFICIAL neural networks; WAVELETS (Mathematics); COMPARATIVE studies; ENVIRONMENTAL degradation; WIND power
- Publication
Mathematical Problems in Engineering, 2013, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2013/395815