Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks

Oleksandr Savchenko,Iryna Trunova,Serhii Halko,Oleksandr Miroshnyk,Sergii Dudnikov,Anatolii Sereda,Oleksandr Kozlovskyi,Oleksandr Moroz,Roman Buinyi

doi:10.1109/khpiweek53812.2021.9570009

Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks

Oleksandr Savchenko, Iryna Trunova + Show 7 more

https://doi.org/10.1109/khpiweek53812.2021.9570009

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Publication Date: Sep 13, 2021

Citations: 6

Affiliation: Kharkiv Petro Vasylenko National Technical University of Agriculture, Tavria State Agrotechnological University, Central Ukrainian National Technical University, Chernihiv Polytechnic National University

#Generation Of Solar Power Plants #Efficiency Of Solar Power Plants + Show 8 more

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Abstract

The method of forecasting of the generation of solar power plants on the basis of the theory of artificial neural networks is proposed. Creating a predictive model has been carried out in the STATISTICA software package. To predict the energy of solar radiation for 24 hours in advance, the so-called “time window” method was used. To predict the intensity of solar radiation, measurements of hydro meteorological station were used. During simulation, the width of the input “time window” was set to 24 hours, the width of the output “time window” was set to 1 hours. Thus, the “time window” was shifted 24 times. The practical results of the article are offered to the use of enterprises operating solar power plants.

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