A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning

doi:10.1063/5.0097757

	A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning
	Wang, Nier; Li, Zhanming
刊名	Journal of Renewable and Sustainable Energy
	2022-07-01
卷号	14 期号:4
关键词	Data handling Decision trees Electric load dispatching Machine learning Weather forecasting Wind farm Ensemble learning Forecasting methods Large amounts Multi-model combination Processing capacities Short-term wind power forecasting Stacking framework Stackings Times series
ISSN号	1941-7012
DOI	10.1063/5.0097757
英文摘要	Aiming at the problem that the traditional wind power forecasting is difficult to deal with a large amount of strong volatility data and limited processing capacity of time series, a wind power forecasting method based on multi-model combination under stacking framework was proposed. First, the wind turbine data are cleaned by density-based spatial clustering of applications with the noise clustering method. Considering the differences of data observation and training principles, the proposed stacking method embedded multiple machine learning algorithms to utilize their diversified strength. The stacking base-learner includes the CBLSTM model, which has the advantages of deep architecture feature extraction, and takes into account data timing and nonlinear relationship as well as XGBoost and other tree ensemble learning models that were suitable for complex data modeling. The feasibility of the algorithm was verified by using the actual wind power data of two wind farms in Northeast and Western China. Experimental results show that the stacking ensemble learning method proposed has better forecasting performance and stability than other single forecasting models, which is of great significance to guide wind power dispatching operation and improve wind power consumption capacity. © 2022 Author(s).
WOS研究方向	Science & Technology - Other Topics ; Energy & Fuels
语种	英语
出版者	American Institute of Physics Inc.
WOS记录号	WOS:000838389600001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159714]
专题	电气工程与信息工程学院
作者单位	College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Wang, Nier,Li, Zhanming. A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning[J]. Journal of Renewable and Sustainable Energy,2022,14(4).
APA	Wang, Nier,&Li, Zhanming.(2022).A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning.Journal of Renewable and Sustainable Energy,14(4).
MLA	Wang, Nier,et al."A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning".Journal of Renewable and Sustainable Energy 14.4(2022).