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

IR > 法学院

	Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target
	Dong, Yunxuan 1; Wang, Jing2 ; Xiao, Ling 3; Fu, Tonglin 4,5,6
	2021-01-15
会议录名称	Energy
卷号	215
出版者	Elsevier Ltd
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
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
DOI	10.1016/j.energy.2020.119180
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Thermodynamics ; Energy & Fuels
WOS类目	Thermodynamics ; Energy & Fuels
WOS记录号	WOS:000596834000005
EI入藏号	20204709513428
EI主题词	Wind
ISSN	03605442
来源库	Compendex
分类代码	443.1 Atmospheric Properties - 615.8 Wind Power (Before 1993, use code 611 ) - 723.1 Computer Programming - 921.5 Optimization Techniques - 921.6 Numerical Methods - 922.2 Mathematical Statistics
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/132721
专题	法学院
作者单位	1.Department of Electrical and Computer Engineering, University of Macau, Macao, China; 2.School of Law, Lanzhou University of Technology, Lanzhou; Gansu; 730050, China; 3.School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing; 400065, China; 4.School of Mathematics & Statistics, LongDong University, Qingyang; Gansu, China; 5.Shapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou; 730000, China; 6.University of Chinese Academy of Sciences, Beijing; 100049, 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[C]:Elsevier Ltd,2021.