Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target

doi:10.1016/j.energy.2020.119180

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	Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target
	Dong, Yunxuan 3; Wang, Jing 4; Xiao, Ling 2; Fu, Tonglin 1,5,6
刊名	Energy
	2021-01-15
卷号	215
关键词	Convergence of numerical methods Convolutional neural networks Cost functions Forecasting Heuristic algorithms Optimization Time series Wind powerComputation complexity Convergence problems Convexity-preserving Heuristic optimization algorithms Multiple-objective optimization Optimal parameter Wind speed forecasting Wind speed time series
ISSN号	03605442
DOI	10.1016/j.energy.2020.119180
英文摘要	Accurate and reliable wind speed forecasting (WSF) is crucial for wind power systems. As one of the effective forecast methods, machine learning (ML) methods are employed for wind speed time series forecasting because the excellent ability in fitting the relationship between data and cost function. However, the cost functions with non-convexity make the whole problem poor interpretability and poor robustness. In this paper, a novel hybrid supervised approach is proposed to solve the above problems. The proposed approach has adopted local convolutional neural networks (LCNNs) for convexity preserving of the cost function, in this way, a non-convex problem can be transformed as a convex problem so that heuristic optimization algorithms is adopted to find optimal parameters, and it helps to construct a more stable model. Highway Gate (HG) algorithm is adopted to decrease the computation complexity of the proposed model. The numerical simulation results indicate that the proposed method is not only effective for solving convergence problem cost by non-convexity, but also beneficial to improve accuracy and stability of the traditional ML for wind speed time series forecasting. © 2020 Elsevier Ltd
WOS研究方向	Thermodynamics ; Energy & Fuels
语种	英语
出版者	Elsevier Ltd
WOS记录号	WOS:000596834000005
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/132386]
专题	法学院
作者单位	1.School of Mathematics & Statistics, LongDong University, Qingyang; Gansu, China; 2.School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing; 400065, China; 3.Department of Electrical and Computer Engineering, University of Macau, Macao, China; 4.School of Law, Lanzhou University of Technology, Lanzhou; Gansu; 730050, China; 5.University of Chinese Academy of Sciences, Beijing; 100049, China 6.Shapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou; 730000, China;
推荐引用方式 GB/T 7714	Dong, Yunxuan,Wang, Jing,Xiao, Ling,et al. Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target[J]. Energy,2021,215.
APA	Dong, Yunxuan,Wang, Jing,Xiao, Ling,&Fu, Tonglin.(2021).Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target.Energy,215.
MLA	Dong, Yunxuan,et al."Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target".Energy 215(2021).