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Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing

文献类型：期刊论文

作者Wang, Yu1,2; Zhu, Changan1; Ye, Xiaodong2; Zhao, Jianghai2; Wang, Deji3
刊名INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
出版日期2021-06-30
卷号35
ISSN号0218-0014
关键词Wind speed spatio-temporal prediction Gaussian random field spatio-temporal kriging ARIMA
DOI10.1142/S021800142159031X
通讯作者Wang, Deji(wangdeji@aliyun.com)
英文摘要It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing condition for them to be smoothed. Meanwhile, further theoretical analysis is provided to discuss why the STOK method is potentially more accurate than the ARIMA method for wind speed time series prediction. Results show that the proposed method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of mean absolute error (MAE) and root-mean-square error (RMSE).
WOS关键词GAUSSIAN PROCESS REGRESSION ; KALMAN FILTER ; DECOMPOSITION ; NETWORK
资助项目Key R&D Program of Jiangsu Province[BE2017007-1] ; Project of National Natural Science Foundation of China[61703390] ; Anhui Natural Science Foundation[1808085QF193]
WOS研究方向Computer Science
语种英语
出版者WORLD SCIENTIFIC PUBL CO PTE LTD
WOS记录号WOS:000672232700014
资助机构Key R&D Program of Jiangsu Province ; Project of National Natural Science Foundation of China ; Anhui Natural Science Foundation
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/123529]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Deji
作者单位1.Univ Sci & Technol China, Dept Precis Machinery & Instrumentat, Hefei 230026, Anhui, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230026, Anhui, Peoples R China
3.Staff Dev Inst CNTC, Zhengzhou 450000, Henan, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yu,Zhu, Changan,Ye, Xiaodong,et al. Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2021,35.
APA Wang, Yu,Zhu, Changan,Ye, Xiaodong,Zhao, Jianghai,&Wang, Deji.(2021).Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing.INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,35.
MLA Wang, Yu,et al."Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing".INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 35(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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