A Hybrid Method for Short-Term Wind Speed Forecasting

被引：26

作者：

Zhang, Jinliang ^{[1
,2
]}

Wei, YiMing ^{[2
,3
]}

Tan, Zhong-fu ^{[1
]}

Wang, Ke ^{[2
,3
]}

Tian, Wei ^{[4
]}

机构：

[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China

[2] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100181, Peoples R China

[3] Beijing Inst Technol, Sch Management & Econ, Beijing 102206, Peoples R China

[4] IIT, Sch Management & Econ, Chicago, IL 60616 USA

来源：

SUSTAINABILITY | 2017年 / 9卷 / 04期

基金：

美国国家科学基金会;

关键词：

short-term wind speed forecasting; ensemble empirical mode decomposition (EEMD); adaptive neural network based fuzzy inference system (ANFIS); seasonal auto-regression integrated moving average (SARIMA); ARTIFICIAL NEURAL-NETWORKS; SPATIAL CORRELATION; POWER-GENERATION; PREDICTION; MODELS; SYSTEM; ENERGY;

D O I：

10.3390/su9040596

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

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.

引用

页数：10

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