气候变化领域集成服务门户(CCPortal): Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm

CCPortal

DOI	10.1016/j.egyr.2021.12.062
	Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm
	Wang, Lili; Guo, Yanlong; Fan, Manhong; Li, Xin
通讯作者	Wang, LL (通讯作者)
发表日期	2022
ISSN	2352-4847
起始页码	1508
结束页码	1518
卷号	8
英文摘要	Wind speed prediction plays an essential role in wind energy utilization. However, most existing studies of wind speed forecasting used data from one location to build models and forecasts, which limited the accuracy of wind speed forecasting. Therefore, to improve the prediction accuracy at a target location, this study proposes a multiple-point model based on data from multiple locations for short-term wind speed prediction. The model, which utilizes wind speed measurements from neighboring locations and combines the extreme learning machine (ELM) with the AdaBoost algorithm, is named the multiple-point-AdaBoost-ELM model. Data from seventeen automatic meteorological stations in the Heihe River Basin are used, four stations at different positions are taken as target stations for multi-time-scale wind speed prediction, and six models and several metrics are involved for comparative analysis and comprehensive evaluation. The results show that: (1) the prediction performance of the proposed multiple-point-AdaBoost-ELM model is significantly superior to that of the compared single-point models; (2) the prediction accuracy of the multiple-point-AdaBoost-ELM model is relatively less affected by the prediction time-scale than that of the corresponding single -point model; and (3) the stations located at the center of multiple stations can obtain more accurate prediction results than those located near the edges of the region. Therefore, the proposed multiple-point-AdaBoost-ELM model is a more promising method than traditional single-point modeling methods. The proposed method fully uses historical wind speed at surrounding locations to enhance the wind speed predictions at target locations, makes up for the deficiency of the wind speed forecasting using data from one location, and expands a new way for wind speed prediction. (C) 2021 The Author(s). Published by Elsevier Ltd.
关键词	VECTOR REGRESSION METHODOLOGY HEIHE RIVER-BASIN NEURAL-NETWORK HYBRID MODEL SELECTION DECOMPOSITION OPTIMIZATION MANAGEMENT MULTISTEP STRATEGY
英文关键词	Wind speed forecasting; Multiple-point information; Extreme learning machine; AdaBoost
语种	英语
WOS研究方向	Energy & Fuels
WOS类目	Energy & Fuels
WOS记录号	WOS:000783859300006
来源期刊	ENERGY REPORTS
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260594
推荐引用方式 GB/T 7714	Wang, Lili,Guo, Yanlong,Fan, Manhong,et al. Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm[J]. 中国科学院青藏高原研究所,2022,8.
APA	Wang, Lili,Guo, Yanlong,Fan, Manhong,&Li, Xin.(2022).Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm.ENERGY REPORTS,8.
MLA	Wang, Lili,et al."Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm".ENERGY REPORTS 8(2022).