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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

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.

Original language	English
Article number	596
Journal	Sustainability (Switzerland)
Volume	9
Issue number	4
DOIs	https://doi.org/10.3390/su9040596
Publication status	Published - 12 Apr 2017

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/su9040596

Cite this

@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

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this