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Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

Author

Listed:
  • Maria Krechowicz

    (Faculty of Management and Computer Modelling, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland)

  • Adam Krechowicz

    (Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland)

  • Lech Lichołai

    (Faculty of Civil Engineering, Environmental Engineering and Architecture, Rzeszow University of Technology, ul. Poznańska 2, 35-959 Rzeszow, Poland)

  • Artur Pawelec

    (Faculty of Management and Computer Modelling, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland)

  • Jerzy Zbigniew Piotrowski

    (Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland)

  • Anna Stępień

    (Faculty of Civil Engineering and Architecture, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland)

Abstract

Problems with inaccurate prediction of electricity generation from photovoltaic (PV) farms cause severe operational, technical, and financial risks, which seriously affect both their owners and grid operators. Proper prediction results are required for optimal planning the spinning reserve as well as managing inertia and frequency response in the case of contingency events. In this work, the impact of a number of meteorological parameters on PV electricity generation in Poland was analyzed using the Pearson coefficient. Furthermore, seven machine learning models using Lasso Regression, K–Nearest Neighbours Regression, Support Vector Regression, AdaBoosted Regression Tree, Gradient Boosted Regression Tree, Random Forest Regression, and Artificial Neural Network were developed to predict electricity generation from a 0.7 MW solar PV power plant in Poland. The models were evaluated using determination coefficient ( R 2 ), the mean absolute error ( M A E ), and root mean square error ( R M S E ). It was found out that horizontal global irradiation and water saturation deficit have a strong proportional relationship with the electricity generation from PV systems. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed PV farm. Random Forest Regression was the most reliable and accurate model, as it received the highest R 2 (0.94) and the lowest M A E (15.12 kWh) and R M S E (34.59 kWh).

Suggested Citation

  • Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4006-:d:827484
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    References listed on IDEAS

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    Cited by:

    1. Tariq Muneer & Mehreen Saleem Gul & Marzia Alam, 2022. "Modelling of a Large Solar PV Facility: England’s Mallard Solar Farm Case Study," Energies, MDPI, vol. 15(22), pages 1-17, November.
    2. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    3. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.

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