FORECASTING THE INTENSITY OF SOLAR RADIATION BASED ON ARTIFICIAL NEURAL NETWORKS

B. Basok, M. P. Novitska, V. Kravchenko
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Abstract

The paper considers short-term forecasting of the intensity of solar radiation in the city of Odessa based on an artificial neural network. The artificial neural network was trained on the experimental data of the ground weather station (Davis 6162EU), which is installed on the roof of the educational building of the Odessa National Polytechnic University. Modeling, validation, and testing of experimental data were performed using the MATLAB software package, namely Neural Network Toolbox. The Levenberg-Markwatt model is used in this work. The analyzed data set was divided into proportions of 70%, 15%, 15% for neural network training, its validation, and testing, respectively. The results which the trained neural network gave during forecasting within the framework of the database and outside it are given. The deviation between real and forecast data is analyzed. The root-mean-square error on December 26, 2016 was 13.03 W / m2, and on December 27, 2016 - 9.44 W / m2 when forecasting outside the database. Evaluation of the accuracy of an artificial neural network has shown its effectiveness in predicting the intensity of solar radiation. To predict parameters based on artificial neural networks, experimental data that describe a real system are needed. Artificial neural networks, like other approximation methods, have both advantages and disadvantages.
基于人工神经网络的太阳辐射强度预测
本文研究了基于人工神经网络对敖德萨市太阳辐射强度的短期预报。人工神经网络是在安装在敖德萨国立理工大学教育大楼屋顶的地面气象站(Davis 6162EU)的实验数据上进行训练的。使用MATLAB软件包Neural Network Toolbox对实验数据进行建模、验证和测试。在这项工作中使用了Levenberg-Markwatt模型。将分析的数据集分成70%、15%、15%的比例,分别用于神经网络的训练、验证和测试。给出了训练后的神经网络在数据库框架内和数据库框架外的预测结果。分析了实际数据与预测数据的偏差。2016年12月26日的均方根误差为13.03 W / m2, 2016年12月27日的外库预测误差为- 9.44 W / m2。对人工神经网络的精度评价表明,它在预测太阳辐射强度方面是有效的。为了基于人工神经网络进行参数预测,需要描述真实系统的实验数据。人工神经网络和其他近似方法一样,既有优点也有缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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