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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

    as
    1. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
    2. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    3. Maria Krechowicz & Adam Krechowicz, 2021. "Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning," Energies, MDPI, vol. 14(2), pages 1-28, January.
    4. Csereklyei, Zsuzsanna & Qu, Songze & Ancev, Tihomir, 2019. "The effect of wind and solar power generation on wholesale electricity prices in Australia," Energy Policy, Elsevier, vol. 131(C), pages 358-369.
    5. Jakub Jurasz & Marcin Wdowikowski & Mariusz Figurski, 2020. "Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data," Energies, MDPI, vol. 13(16), pages 1-10, August.
    6. Mario Tovar & Miguel Robles & Felipe Rashid, 2020. "PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México," Energies, MDPI, vol. 13(24), pages 1-15, December.
    7. Leidy Gutiérrez & Julian Patiño & Eduardo Duque-Grisales, 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction," Energies, MDPI, vol. 14(15), pages 1-16, July.
    8. Radziemska, E., 2003. "The effect of temperature on the power drop in crystalline silicon solar cells," Renewable Energy, Elsevier, vol. 28(1), pages 1-12.
    9. Agata Zdyb & Slawomir Gulkowski, 2020. "Performance Assessment of Four Different Photovoltaic Technologies in Poland," Energies, MDPI, vol. 13(1), pages 1-17, January.
    10. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    11. Dong, Jin & Olama, Mohammed M. & Kuruganti, Teja & Melin, Alexander M. & Djouadi, Seddik M. & Zhang, Yichen & Xue, Yaosuo, 2020. "Novel stochastic methods to predict short-term solar radiation and photovoltaic power," Renewable Energy, Elsevier, vol. 145(C), pages 333-346.
    12. Bigorajski, Jarosław & Chwieduk, Dorota, 2019. "Analysis of a micro photovoltaic/thermal – PV/T system operation in moderate climate," Renewable Energy, Elsevier, vol. 137(C), pages 127-136.
    13. Maria Krechowicz & Jerzy Zbigniew Piotrowski, 2021. "Comprehensive Risk Management in Passive Buildings Projects," Energies, MDPI, vol. 14(20), pages 1-25, October.
    14. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
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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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