Open AccessA neural network based computational model to predict the output power of different types of photovoltaic cells
Author(s) -
Wenbo Xiao,
Gizario,
Huaming Wu,
HuaMing Zhang,
Feng Cheng
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0184561
Subject(s) - photovoltaic system , artificial neural network , amorphous solid , amorphous silicon , crystalline silicon , power (physics) , layer (electronics) , biological system , materials science , computer science , silicon , artificial intelligence , pattern recognition (psychology) , optoelectronics , nanotechnology , crystallography , physics , chemistry , electrical engineering , thermodynamics , engineering , biology
In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.