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

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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)

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%.

Suggested Citation

  • Grzegorz Drałus & Damian Mazur & Jacek Kusznier & Jakub Drałus, 2023. "Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation," Energies, MDPI, vol. 16(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6697-:d:1242842
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    1. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    2. Grzegorz Dec & Grzegorz Drałus & Damian Mazur & Bogdan Kwiatkowski, 2021. "Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic," Energies, MDPI, vol. 14(6), pages 1-16, March.
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    1. Jiahui Wang & Mingsheng Jia & Shishi Li & Kang Chen & Cheng Zhang & Xiuyu Song & Qianxi Zhang, 2024. "Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model," Sustainability, MDPI, vol. 16(7), pages 1-24, March.

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