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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)
作者部门工业控制网络与系统研究室
会议名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期June 8-12, 2015
会议地点Shenyang, China
会议录名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码779-783
收录类别EI ; CPCI(ISTP)
EI收录号20161402187594
WOS记录号WOS:000380502300148
产权排序1
ISSN号2379-7711
ISBN号978-1-4799-8730-6
关键词Bp Neural Networks Day-ahead Photovoltaic Forecasting Extreme Learning Machine
摘要The 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.
语种英语
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被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/17389
专题工业控制网络与系统研究室
通讯作者Li ZW(李忠文)
作者单位1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
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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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