Wind speed forecasting by wavelet neural networks: a comparative study
type: Item
instance of: scholarly article
title: Wind speed forecasting by wavelet neural networks: a comparative study
zbMATH Open document ID: 1299.86028
DOI: 10.1155/2013/395815
author: Chuanan Yao
author: Xiankun Gao
author: Yongchang Yu
published in: Mathematical Problems in Engineering
publication date: 2014-11-24T00:00:00Z
review text: Summary: Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting 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 hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North 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.
Mathematics Subject Classification ID: 86A32
zbMATH DE Number: 6372261
Wikidata QID: Q59024411
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/395815
OpenAlex ID: W2056849278
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description: scientific article; zbMATH DE number 6372261