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A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting

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  • Shab Gbémou

    (Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
    PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France)

  • Julien Eynard

    (Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
    PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France)

  • Stéphane Thil

    (Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
    PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France)

  • Emmanuel Guillot

    (PROMES-CNRS (UPR 8521), 7 Rue du Four Solaire, 66120 Font-Romeu-Odeillo-Via, France)

  • Stéphane Grieu

    (Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
    PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France)

Abstract

The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10 min are used to train the models and forecast GHI at varying time horizons, ranging from 10 min to 4 h . Persistence on the clear-sky index, also known as scaled persistence model, is included in this paper as a reference model. Three criteria are used for in-depth performance estimation: normalized root mean square error (nRMSE), dynamic mean absolute error (DMAE) and coverage width-based criterion (CWC). Results confirm that machine learning-based methods outperform the scaled persistence model. The best-performing machine learning-based methods included in this comparative study are the long short-term memory (LSTM) neural network and the GPR model using a rational quadratic kernel with automatic relevance determination.

Suggested Citation

  • Shab Gbémou & Julien Eynard & Stéphane Thil & Emmanuel Guillot & Stéphane Grieu, 2021. "A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting," Energies, MDPI, vol. 14(11), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3192-:d:565352
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    References listed on IDEAS

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    6. Hossam Fraihat & Amneh A. Almbaideen & Abdullah Al-Odienat & Bassam Al-Naami & Roberto De Fazio & Paolo Visconti, 2022. "Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan," Future Internet, MDPI, vol. 14(3), pages 1-24, March.
    7. Alfonso Angel Medina-Santana & Leopoldo Eduardo Cárdenas-Barrón, 2022. "Optimal Design of Hybrid Renewable Energy Systems Considering Weather Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 15(23), pages 1-28, November.
    8. Abdel-Rahman Hedar & Majid Almaraashi & Alaa E. Abdel-Hakim & Mahmoud Abdulrahim, 2021. "Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces," Energies, MDPI, vol. 14(23), pages 1-29, November.
    9. Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "An Ensemble Approach for Intra-Hour Forecasting of Solar Resource," Energies, MDPI, vol. 16(18), pages 1-12, September.
    10. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).

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