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

doi:10.1155/2021/6638436

CORC > 兰州理工大学 > 兰州理工大学 > 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, Shaoyong 3
刊名	COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
	2021-08-27
卷号	2021
ISSN号	1687-5265
DOI	10.1155/2021/6638436
英文摘要	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.
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
语种	英语
出版者	HINDAWI LTD
WOS记录号	WOS:000745942200001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/155012]
专题	土木工程学院
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).