A hybrid method for short-term wind speed forecasting

Jinliang Zhang; Yi Ming Wei; Zhong fu Tan; Ke Wang; Wei Tian

doi:10.3390/su9040596

A hybrid method for short-term wind speed forecasting

Jinliang Zhang^*, Yi Ming Wei, Zhong fu Tan, Ke Wang, Wei Tian

^*此作品的通讯作者

科研成果: 期刊稿件 › 文章 › 同行评审

38 引用（Scopus）

摘要

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.

源语言	英语
文章编号	596
期刊	Sustainability (Switzerland)
卷	9
期	4
DOI	https://doi.org/10.3390/su9040596
出版状态	已出版 - 12 4月 2017

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.3390/su9040596

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, J., Wei, Y. M., Tan, Z. F., Wang, K., & Tian, W. (2017). A hybrid method for short-term wind speed forecasting. Sustainability (Switzerland), 9(4), 文章 596. https://doi.org/10.3390/su9040596

@article{b427c910c16d4b9ab765846442385f62,

title = "A hybrid method for short-term wind speed forecasting",

abstract = "The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.",

keywords = "Adaptive neural network based fuzzy inference system (ANFIS), Ensemble empirical mode decomposition (EEMD), Seasonal auto-regression integrated moving average (SARIMA), Short-term wind speed forecasting",

author = "Jinliang Zhang and Wei, {Yi Ming} and Tan, {Zhong fu} and Ke Wang and Wei Tian",

note = "Publisher Copyright: {\textcopyright} 2017 by the authors.",

year = "2017",

month = apr,

day = "12",

doi = "10.3390/su9040596",

language = "English",

volume = "9",

journal = "Sustainability (Switzerland)",

issn = "2071-1050",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "4",

}

TY - JOUR

T1 - A hybrid method for short-term wind speed forecasting

AU - Zhang, Jinliang

AU - Wei, Yi Ming

AU - Tan, Zhong fu

AU - Wang, Ke

AU - Tian, Wei

PY - 2017/4/12

Y1 - 2017/4/12

N2 - The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.

AB - The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.

KW - Adaptive neural network based fuzzy inference system (ANFIS)

KW - Ensemble empirical mode decomposition (EEMD)

KW - Seasonal auto-regression integrated moving average (SARIMA)

KW - Short-term wind speed forecasting

UR - http://www.scopus.com/inward/record.url?scp=85018463690&partnerID=8YFLogxK

U2 - 10.3390/su9040596

DO - 10.3390/su9040596

M3 - Article

AN - SCOPUS:85018463690

SN - 2071-1050

VL - 9

JO - Sustainability (Switzerland)

JF - Sustainability (Switzerland)

IS - 4

M1 - 596

ER -

A hybrid method for short-term wind speed forecasting

摘要

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此