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Day-ahead hourly photovoltaic generation forecasting using extreme learning machine
Li ZW(李忠文); Zang CZ(臧传治); Zeng P(曾鹏); Yu HB(于海斌); Li HP(李鹤鹏)
Department工业控制网络与系统研究室
Conference Name2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Conference DateJune 8-12, 2015
Conference PlaceShenyang, China
Source Publication2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages779-783
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161402187594
WOS IDWOS:000380502300148
Contribution Rank1
ISSN2379-7711
ISBN978-1-4799-8730-6
KeywordBp Neural Networks Day-ahead Photovoltaic Forecasting Extreme Learning Machine
AbstractThe photovoltaic (PV) generation systems as environmentally friendly renewable energy sources are increasing. However, the power generation of solar has high uncertainty and intermittency and brings significant challenges to power system operators. The accurate forecasting of photovoltaic (PV) power production is good for both the grid and individual smart homes. In this paper, we propose a novel weather-based photovoltaic generation forecasting approach using extreme learning machine (ELM) for 1-day ahead hourly forecasting of PV power output. In the proposed approach, the weather conditions are divided into three types which are sunny day, cloudy day, and rainy day and training the PV power output forecasting models separately for those three weather types. In this paper, we take the PV output history data from the PV experiment system located in Shanghai for case study. The forecasting results show that the proposed model outperform the BP neural networks model in all three weather types.
Language英语
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17389
Collection工业控制网络与系统研究室
Corresponding AuthorLi ZW(李忠文)
Affiliation1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Li ZW,Zang CZ,Zeng P,et al. Day-ahead hourly photovoltaic generation forecasting using extreme learning machine[C]. Piscataway, NJ, USA:IEEE,2015:779-783.
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