人工神经网络
可再生能源
发电机(电路理论)
发电
航程(航空)
光伏
热电发电机
能量(信号处理)
光伏系统
计算机科学
功率(物理)
工程类
汽车工程
电气工程
数学
物理
人工智能
统计
航空航天工程
热力学
热电效应
量子力学
作者
Florencia Almonacid,Catalina Rus-Casas,P. Pérez-Higueras,L. Hontoria
出处
期刊:Energy
[Elsevier]
日期:2010-11-21
卷期号:36 (1): 375-384
被引量:134
标识
DOI:10.1016/j.energy.2010.10.028
摘要
The use of photovoltaics for electricity generation purposes has recorded one of the largest increases in the field of renewable energies. The energy production of a grid-connected PV system depends on various factors. In a wide sense, it is considered that the annual energy provided by a generator is directly proportional to the annual radiation incident on the plane of the generator and to the installed nominal power. However, a range of factors is influencing the expected outcome by reducing the generation of energy. The aim of this study is to compare the results of four different methods for estimating the annual energy produced by a PV generator: three of them are classical methods and the fourth one is based on an artificial neural network developed by the R&D Group for Solar and Automatic Energy at the University of Jaen. The results obtained shown that the method based on an artificial neural network provides better results than the alternative classical methods in study, mainly due to the fact that this method takes also into account some second order effects, such as low irradiance, angular and spectral effects.
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