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首页> 外文期刊>International Journal of Photoenergy >Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks
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Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks

机译：学习过程预测每日全球，直接和扩散太阳辐照的日常全球辐射与人工神经网络

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摘要

This paper presents three different topologies of feed forward neural network (FFNN) models for generating global, direct, and diffuse hourly solar irradiance in the city of Fez (Morocco). Results from this analysis are crucial for the conception of any solar energy system. Especially, for the concentrating ones, as direct component is seldom measured. For the three models, the main input was the daily global irradiation with other radiometric and meteorological parameters. Three years of hourly data were available for this study. For each solar component's prediction, different combinations of inputs as well as different numbers of hidden neurons were considered. To evaluate these models, the regression coefficient (R-2) and normalized root mean square error (nRMSE) were used. The test of these models over unseen data showed a good accuracy and proved their generalization capability (nRMSE = 13.1%, 9.5%, and 8.05% and R = 0.98, 0.98, and 0.99) for hourly global, hourly direct, and daily direct radiation, respectively. Different comparison analyses confirmed that (FFNN) models surpass other methods of estimation. As such, the proposed models showed a good ability to generate different solar components from daily global radiation which is registered in most radiometric stations.
机译：本文介绍了饲料前进神经网络（FFNN）模型的三种不同拓扑，用于在FEZ（摩洛哥市）中产生全球，直接和漫游的每小时太阳辐照度。该分析的结果对于任何太阳能系统的概念至关重要。特别是，对于浓缩，作为直接组分很少测量。对于三种型号，主要输入是与其他辐射和气象参数的每日全球辐照。这项研究可获得三年的小时数据。对于每个太阳能组件的预测，考虑了不同的输入的不同组合以及不同数量的隐藏神经元。为了评估这些模型，使用回归系数（R-2）和归一化的根均方误差（NRMSE）。通过看不见的数据测试这些模型显示出良好的准确性，并证明了他们的概括能力（NRMSE = 13.1％，9.5％，8.05％，0.98,0.98,0.98,0.99），用于每小时全球，每小时直接和日常直接辐射， 分别。不同的比较分析证实（FFNN）模型超越了其他估计方法。因此，所提出的模型显示出从在大多数辐射站中登记的日常全球辐射产生不同的太阳能元件的良好能力。

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