Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation

Grzegorz Drałus, Damian Mazur, Jacek Kusznier () and Jakub Drałus
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Grzegorz Drałus: Department of Electrical and Computer Engineering Fundamentals, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
Damian Mazur: Department of Electrical and Computer Engineering Fundamentals, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
Jacek Kusznier: Department of Photonics, Electronics and Lighting Technology, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland
Jakub Drałus: Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland

Energies, 2023, vol. 16, issue 18, 1-23

Abstract: This paper presents the models developed for the short-term forecasting of energy production by photovoltaic panels. An analysis of a set of weather factors influencing daily energy production is presented. Determining the correlation between the produced direct current (DC) energy and the individual weather parameters allowed the selection of the potentially best explanatory factors, which served as input data for the neural networks. The forecasting models were based on MLP and Elman-type networks. An appropriate selection of structures and learning parameters was carried out, as well as the process of learning the models. The models were built based on different time periods: year-round, semi-annual, and seasonal. The models were developed separately for monocrystalline and amorphous photovoltaic modules. The study compared the models with the predicted and measured insolation energy. In addition, complex forecasting models were developed for the photovoltaic system, which could forecast DC and AC energy simultaneously. The complex models were developed according to the rules of global and local modeling. The forecast errors of the developed models were included. The smallest values of the DC energy forecast errors were achieved for the models designed for summer forecasts. The percentage forecast error was 1.95% using directly measured solar irradiance and 5. 57% using predicted solar irradiance. The complex model for summer forecasted the AC energy with an error of 1.86%.

Keywords: forecasting of PV power production; weather factors; photovoltaic systems; neural networks; complex system; PV energy management; AI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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