Day-ahead hourly photovoltaic generation forecasting using extreme learning machine
文献类型：会议论文
| 作者 | Li ZW(李忠文) ; Zang CZ(臧传治); Zeng P(曾鹏); Yu HB(于海斌); Li HP(李鹤鹏)
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| 出版日期 | 2015 |
| 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 |
| 关键词 | BP Neural Networks Day-ahead Photovoltaic Forecasting Extreme Learning Machine |
| 页码 | 779-783 |
| 中文摘要 | 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. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议录 | 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
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| 会议录出版者 | IEEE |
| 会议录出版地 | Piscataway, NJ, USA |
| 语种 | 英语 |
| ISSN号 | 2379-7711 |
| ISBN号 | 978-1-4799-8730-6 |
| WOS记录号 | WOS:000380502300148 |
| 源URL | [http://ir.sia.cn/handle/173321/17389]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 推荐引用方式 GB/T 7714 | Li ZW,Zang CZ,Zeng P,et al. Day-ahead hourly photovoltaic generation forecasting using extreme learning machine[C]. 见:2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Shenyang, China. June 8-12, 2015. |
入库方式： OAI收割
来源：沈阳自动化研究所
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