Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

被引：267

作者：

Liu, Hui ^{[1
,2
]}

Tian, Hong-qi ^{[1
]}

Pan, Di-fu ^{[1
]}

Li, Yan-fei ^{[1
,2
]}

机构：

[1] Cent S Univ, Sch Traff & Transportat Engn, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Hunan, Peoples R China

[2] Univ Rostock, Fac Informat & Elect Engn, Inst Automat, D-18119 Rostock, Mecklenburg Vor, Germany

来源：

APPLIED ENERGY | 2013年 / 107卷

基金：

中国国家自然科学基金;

关键词：

Wind speed predictions; Wind speed forecasting; Hybrid model; Signal decomposition; ANN; ARIMA; PREDICTION; ANN;

D O I：

10.1016/j.apenergy.2013.02.002

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BEGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BEGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：191 / 208

页数：18

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