Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System

doi:10.1155/2021/6638436

IR > 土木工程学院

	Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System
	Wu, Dongchun 1; Kan, Jiarong 1; Lin, Hsiung-Cheng 2; Li, Shaoyong3
	2021-08-27
发表期刊	COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
ISSN	1687-5265
卷号	2021
摘要	When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.
DOI	10.1155/2021/6638436
收录类别	SCIE
语种	英语
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000745942200001
出版者	HINDAWI LTD
来源库	WOS
引用统计	被引频次：1[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/155012
专题	土木工程学院
通讯作者	Lin, Hsiung-Cheng
作者单位	1.Yancheng Inst Technol, Coll Elect Engn, Yancheng 224051, Peoples R China; 2.Natl Chin Yi Univ Technol, Dept Elect Engn, Taichung 41170, Taiwan; 3.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Wu, Dongchun,Kan, Jiarong,Lin, Hsiung-Cheng,et al. Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2021,2021.
APA	Wu, Dongchun,Kan, Jiarong,Lin, Hsiung-Cheng,&Li, Shaoyong.(2021).Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2021.
MLA	Wu, Dongchun,et al."Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021(2021).

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