FORECASTING THE INTENSITY OF SOLAR RADIATION BASED ON ARTIFICIAL NEURAL NETWORKS
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.
2. М.А. Novotarskii, B.B. Nesterenko. [Artificial neural network: computation ] Pratsi Instytutu matematyky NAN Ukrainy [ Proceedings of the Institute of mathematics NAS of Ukraine], Vol.50. Kyiv: Institute of mathematics NAS of Ukraine. 2004. 408 с.(ukr)
3. Fermín Rodríguez, Alice Fleetwood, Ainhoa Galarza, Luis Fontán. Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control. Renewable Energy. 2018. Volume 126. P. 855-864, https://doi.org/10.1016/j.renene.2018.03.070.
4. Hameed W.I., Sawadi B.A., Al-Kamil S.J., Al-Radhi M.S., Al-Yasir Y.I.A., Saleh A.L., Abd-Alhameed R.A. Prediction of Solar Irradiance Based on Artificial Neural Networks. Inventions . 2019. N3(4), 45. https://doi.org/10.3390/inventions4030045
5. Ümmühan Başaran Filik, Tansu Filik. Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir. Energy Procedia. 2017. Vol.107. P. 264-269. https://doi.org/10.1016/j.egypro.2016.12.147.
6. Hernández Luis, Baladrón Carlos, Aguiar Javier M., Calavia Lorena, Carro Belén, Sánchez-Esguevillas Antonio, García Pablo, Lloret Jaime. Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks. 2013. Energies 6, no. 6. P. 2927-2948. https://doi.org/10.3390/en6062927
7. Soteris A. Kalogirou. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews. 2001. Volume 5, Issue 4. P. 373-401. https://doi.org/10.1016/S1364-0321(01)00006-5
8. Basok B.I., Veremiichuk Yu.A. [Resource potential еstimation of solar power generation in Odessa region] Kyiv, Vidavnichii dim “Kalita” [Publishing house “Kalita”]. 2018. 250 p. (Ukr)
9. Kravchenko, V.P., Kravchenko I.V., Bondar I.V. [Instrumental determination of insolation for city Odessa] Enerhetika: ekonomika, tekhnologii, ekolohiia [Power engineering: economics, technique, ecology]. 2016. № 1 (43). p. 20–27. (Ukr)
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