Short Term Power Prediction of the Photovoltaic Power Station Based on Power Profiles

被引：0
|
作者：
Radvansky, Martin [1 ]
Kudelka, Milos [1 ]
Snasel, Vaclav [1 ]
机构：
[1] Tech Univ Ostrava, VSB, Ostrava, Czech Republic
来源：
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014) | 2014年 / 303卷
关键词：
prediction; photovoltaic power station; power profiles; SOLAR-RADIATION;
D O I：
10.1007/978-3-319-08156-4_38
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The use of solar energy has undergone rapid development in recent years. Photovoltaic power stations (PVPS) are often used as a source of power for smart off-grid houses. Integration of this kind of energy source is challenging because it is a source of variably generated power due to meteorological uncertainty. In this paper, we present results of the short term prediction method of generated power for small PVPS based on self-organizing maps and previously introduced power profiles.
引用
收藏
页码：383 / 393
页数：11
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