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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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    1. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    2. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
    3. Jung Youn Mo & Wooyoung Jeon, 2017. "How Does Energy Storage Increase the Efficiency of an Electricity Market with Integrated Wind and Solar Power Generation?—A Case Study of Korea," Sustainability, MDPI, vol. 9(10), pages 1-15, October.
    4. Naowarat Tephiruk & Weerawoot Kanokbannakorn & Thongchart Kerdphol & Yasunori Mitani & Komsan Hongesombut, 2018. "Fuzzy Logic Control of a Battery Energy Storage System for Stability Improvement in an Islanded Microgrid," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    5. Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    6. Myada Shadoul & Hassan Yousef & Rashid Al Abri & Amer Al-Hinai, 2021. "Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters," Energies, MDPI, vol. 14(4), pages 1-22, February.
    7. En-Chih Chang, 2018. "Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters," Energies, MDPI, vol. 11(10), pages 1-14, September.
    8. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
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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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