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Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

Author

Listed:
  • Grzegorz Dec

    (Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Grzegorz Drałus

    (Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Damian Mazur

    (Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Bogdan Kwiatkowski

    (Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

Abstract

This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1676-:d:519235
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    References listed on IDEAS

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    Cited by:

    1. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
    2. Marek Pavlík & L’ubomír Beňa & Dušan Medved’ & Zsolt Čonka & Michal Kolcun, 2023. "Analysis and Evaluation of Photovoltaic Cell Defects and Their Impact on Electricity Generation," Energies, MDPI, vol. 16(6), pages 1-16, March.
    3. 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.
    4. Dusan Medved & Lubomir Bena & Maksym Oliinyk & Jaroslav Dzmura & Damian Mazur & David Martinko, 2023. "Assessing the Effects of Smart Parking Infrastructure on the Electrical Power System," Energies, MDPI, vol. 16(14), pages 1-16, July.

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