兰州大学机构库 >数学与统计学院
A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China
Qin, Shanshan1,2; Wang, Jianzhou3; Wu, Jie1; Zhao, Ge4
2016
Source PublicationInternational Journal of Green Energy   Impact Factor & Quartile
ISSN1543-5075
Volume13Issue:6Pages:595-607
AbstractWind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000-2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.
KeywordElman artificial neural network (EANN) error correction smooth transition periodic autoregressive (STPAR) wind energy wind speed forecasting
Subject AreaThermodynamics ; Science & Technology - Other Topics ; Energy & Fuels
PublisherTAYLOR & FRANCIS
DOI10.1080/15435075.2014.961462
Publication PlacePHILADELPHIA
Indexed ByEI ; SCIE
First Inst
Funding Project国家自然科学基金项目 ; 国家留学基金委项目
Project NumberNational Natural Science Foundation of China [71171102] ; China Scholarship Council
WOS IDWOS:000380149200009
Funding OrganizationNSFC ; CSC
EI Accession Number20162802589585
SubtypeArticle
EI KeywordsElectric power generation ; Error correction ; Forecasting ; Neural networks ; Speed ; Sustainable development ; Wind power
EI Classification NumberAtmospheric Properties:443.1 ; Wind Power (Before 1993, use code 611 ):615.8
Original Document TypeJournal article (JA)
IRIDWOS:000380149200009
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/180652
Collection数学与统计学院
Corresponding AuthorWang, Jianzhou
Affiliation
1.School of Mathematics and Statistics, Lanzhou University, Lanzhou, China
2.Department of Mathematics and Statistics, York University, Toronto; ON, Canada
3.School of Statistics, Dongbei University of Finance and Economics, Dalian, China
4.Department of Statistics, University of South Carolina, Columbia; SC, United States
First Author AffilicationSchool of Mathematics and Statistics
Recommended Citation
GB/T 7714
Qin, Shanshan,Wang, Jianzhou,Wu, Jie,et al. A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China[J]. International Journal of Green Energy,2016,13(6):595-607.
APA Qin, Shanshan,Wang, Jianzhou,Wu, Jie,&Zhao, Ge.(2016).A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China.International Journal of Green Energy,13(6),595-607.
MLA Qin, Shanshan,et al."A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China".International Journal of Green Energy 13.6(2016):595-607.
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