A model based on artificial neuronal network for the prediction of the maximum power of a low concentration photovoltaic module for building integration
A model based on artificial neuronal network for the prediction of the maximum power of a low concentration photovoltaic module for building integration
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DOI：
10.1016/j.solener.2013.11.036
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发表时间：
2014-02-01
期刊：
影响因子：
6.7
通讯作者：
Perez-Higueras, P.
中科院分区：
文献类型：
--
作者：
Fernandez, Eduardo F.;Almonacid, F.;Perez-Higueras, P.
低聚光光伏（LCPV）组件的建筑一体化被认为是有很大的潜力，因为它提供了几个传统的光伏技术的优势。然而，这种技术的问题之一是，到目前为止，在文献中还没有模型来直接计算这些类型的系统的最大功率。模型的开发是促进该技术应用的重要任务，因为它允许预测能量产量。本文提出了一种基于人工神经网络的模型来解决这一重要问题。该模型考虑了影响这些类型系统的电输出的所有主要重要参数：直接辐照度、漫射辐照度、模块温度以及横向和纵向入射角。结果表明，所提出的模型可以用来估计的LCPV模块的最大功率，以建筑一体化具有足够的误差幅度。(C)2013爱思唯尔有限公司保留所有权利。
Low concentration photovoltaic (LCPV) modules for building integration are considered to have great potential because it offers several advantages over conventional photovoltaic technology. However, one of the problems of this technology is that as yet there are no models in the literature to directly calculate the maximum power of these kinds of systems. The development of models is an important task to promote the application of this technology because it allows the prediction of the energy yield. In this paper a model based on artificial neural networks has been developed to address this important issue. The model takes into account all the main important parameters that influence the electrical output of these kinds of systems: direct irradiance, diffuse irradiance, module temperature and the transverse and longitudinal incidence angles. The results show that the proposed model can be used for estimating the maximum power of a LCPV module for building integration with an adequate margin of error. (C) 2013 Elsevier Ltd. All rights reserved.