Xiao WenBo, Nazario Gina, Wu HuaMing, Zhang HuaMing, Cheng Feng
Jiangxi Engineering Laboratory for Optoelectronics Testing Technology, Nanchang Hangkong University, Nanchang, China.
Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, United States of America.
PLoS One. 2017 Sep 12;12(9):e0184561. doi: 10.1371/journal.pone.0184561. eCollection 2017.
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.
在本文中，我们介绍了一种基于人工神经网络（ANN）的计算模型，用于预测三种类型的光伏电池，即单晶、多晶和非晶硅光伏电池的输出功率。预测结果与实验数据非常接近，并且还受到隐藏神经元数量的影响。受外部条件影响的太阳能发电功率输出顺序从小到大依次为：多晶硅、单晶硅和非晶硅电池。此外，还研究了功率预测对隐藏神经元数量的依赖性。对于多晶和非晶硅电池，三个或四个隐藏层单元导致高相关系数和低均方误差。对于单晶硅电池，在隐藏层单元为8时取得了最佳结果。
