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A Neural Network Approach for Generating Solar Irradiation Artificial Series

机译：人工太阳辐射序列生成的神经网络方法

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摘要

In this paper a relevant problem in the photovoltaic solar energy field is considered: the generation of artificial series of hourly solar irradiation. The proposed methodology artificially generates series following the average tendency of the hourly radiation series k_t in a given place. This is obtained by making use of a set of historical values of this series in such place (for training purposes) as well as the daily clarity index D_T of the year to be generated. This information is employed for the supervised training of a proposed neural network model. The neural model employs a well known paradigm, called Multilayer Perceptron (MLP), in a feedback architecture. The generation method is based on the MLP ability to extract, from a sufficiently general training set, the existing relationships between variables whose interdependence is unknown a priori. This way, the presenteddesign methodology can implicitly include all the available information. Simulation results show the good performance of the irradiation series generator, and the general applicability of this methodology in the estimation of higly complex temporal series.
机译：本文考虑了光伏太阳能领域中的一个相关问题：每小时太阳辐射的人工序列的产生。所提出的方法根据给定位置的每小时辐射序列k_t的平均趋势人工生成序列。这是通过在该位置（出于培训目的）使用该系列的一组历史值以及要生成的当年的每日清晰度指标D_T来获得的。该信息用于拟议的神经网络模型的监督训练。神经模型在反馈体系结构中采用了众所周知的范例，称为多层感知器（MLP）。生成方法基于MLP从足够通用的训练集中提取先验未知的相互依存性变量之间的现有关系的能力。这样，所提出的设计方法可以隐式地包括所有可用信息。仿真结果表明辐照序列发生器具有良好的性能，并且该方法在高复杂时间序列的估计中具有普遍的适用性。

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