Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation

doi:10.1016/j.enconman.2015.02.020

	Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation
	Jiang, He 1,2; Dong, Yao 2; Wang, Jianzhou 3; Li, Yuqin 4
	2015-05
发表期刊	ENERGY CONVERSION AND MANAGEMENT
ISSN	0196-8904
卷号	95 页码:42-58
摘要	Due to the scarcity of equipment and the high costs of maintenance, far fewer observations of solar radiation are made than observations of temperature, precipitation and other weather factors. Therefore, it is increasingly important to study several relevant meteorological factors to accurately forecast solar radiation. For this research, monthly average global solar radiation and 12 meteorological parameters from 1998 to 2010 at four sites in the United States were collected. Pearson correlation coefficients and Apriori association rules were successfully used to analyze correlations between the data, which provided a basis for these relative parameters as input variables. Two effective and innovative methods were developed to forecast monthly average global solar radiation by converting a RBF neural network into a multiple linear regression problem, adding a hard-ridge penalty to reduce the number of nodes in the hidden layer, and applying intelligent optimization algorithms, such as the cuckoo search algorithm (CS) and differential evolution (DE), to determine the optimal center and scale parameters. The experimental results show that the proposed models produce much more accurate forecasts than other models. (C) 2015 Elsevier Ltd. All rights reserved.
关键词	Global solar radiation forecasting RBF neural network Hard-ridge penalty Cuckoo search algorithm Differential evolution
DOI	10.1016/j.enconman.2015.02.020
收录类别	SCI ; SCIE
语种	英语
资助项目	National Natural Science Foundation of China[71171102/G0107]
WOS研究方向	Thermodynamics ; Energy & Fuels ; Mechanics
WOS类目	Thermodynamics ; Energy & Fuels ; Mechanics
WOS记录号	WOS:000352169300005
出版者	PERGAMON-ELSEVIER SCIENCE LTD
EI入藏号	20150900579111
EI主题词	Evolutionary algorithms
EI分类号	657.1 Solar Energy and Phenomena - 921.5 Optimization Techniques - 922.2 Mathematical Statistics
引用统计	被引频次：55[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/34072
专题	兰州理工大学计算机与通信学院
通讯作者	Dong, Yao
作者单位	1.Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA; 2.Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China; 3.Dongbei Univ Finance & Econ, Sch Stat, Dalian 116025, Peoples R China; 4.Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Jiang, He,Dong, Yao,Wang, Jianzhou,et al. Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation[J]. ENERGY CONVERSION AND MANAGEMENT,2015,95:42-58.
APA	Jiang, He,Dong, Yao,Wang, Jianzhou,&Li, Yuqin.(2015).Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation.ENERGY CONVERSION AND MANAGEMENT,95,42-58.
MLA	Jiang, He,et al."Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation".ENERGY CONVERSION AND MANAGEMENT 95(2015):42-58.