A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

doi:10.15837/ijccc.2020.4.3901

	A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning
	Wang, Y. S.1,2,3,4; Gao, J.1,2; Xu, Z. W.3,4,5; Luo, J. D.6; Li, L. X.3,4
	2020-08-01
发表期刊	INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
ISSN	1841-9836
卷号	15 期号:4 页码:18
摘要	The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.
关键词	wind power output power ultra-short-term prediction deep learning (DL) long short-term memory (LSTM) model
DOI	10.15837/ijccc.2020.4.3901
收录类别	SCI
语种	英语
资助项目	Inner Mongolia Science and Technology Major Special Projects[2019ZD016] ; Natural Science Foundation of China[61462070] ; Natural Science Foundation of China[61962045] ; Natural Science Foundation of China[61502255] ; Natural Science Foundation of China[61650205] ; Inner Mongolia Agricultural University Doctoral Scientific Research Fund Project[BJ09-44] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2019MS03014] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2018MS-06003] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2019MS06027] ; Inner Mongolia Key Technological Development Program[2019ZD015] ; Key Scientific and Technological Research Program of Inner Mongolia Autonomous Region[2019GG273]
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Information Systems
WOS记录号	WOS:000540296700008
出版者	CCC PUBL-AGORA UNIV
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15235
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gao, J.
作者单位	1.Inner Mongolia Agr Univ, Coll Comp & Informat Engn, Hohhot 010018, Peoples R China 2.Inner Mongolia Autonomous Reg Key Lab Big Data Re, Hohhot 010018, Peoples R China 3.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China 4.Inner Mongolia Autonomous Reg Engn & Technol Res, Hohhot 010080, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 6.Haohan Data Technol Co Ltd, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, Y. S.,Gao, J.,Xu, Z. W.,et al. A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning[J]. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL,2020,15(4):18.
APA	Wang, Y. S.,Gao, J.,Xu, Z. W.,Luo, J. D.,&Li, L. X..(2020).A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning.INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL,15(4),18.
MLA	Wang, Y. S.,et al."A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning".INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 15.4(2020):18.