FORECASTING THE INTENSITY OF SOLAR RADIATION BASED ON ARTIFICIAL NEURAL NETWORKS


  • B.I. Basok Institute of Engineering Thermophysics of the National Academy of Sciences of Ukraine
  • M.P. Novitska Institute of Engineering Thermophysics of the National Academy of Sciences of Ukraine
  • V.P. Kravchenko Odesa National Polytechnic University
Keywords: artificial neural network, insolation, solar radiation intensity, forecasting, renewable energy sources.

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.

References

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Published
2021-02-23
How to Cite
Basok, B., Novitska, M., & Kravchenko, V. (2021). FORECASTING THE INTENSITY OF SOLAR RADIATION BASED ON ARTIFICIAL NEURAL NETWORKS. Thermophysics and Thermal Power Engineering, 43(2), 60-67. https://doi.org/https://doi.org/10.31472/ttpe.2.2021.7
Section
District and Industrial Heat Power, Renewable Energy Systems, Energy Efficiency

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