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A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

Author

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  • Zhaoxuan Li

    (Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
    These authors contributed equally to this work.)

  • SM Mahbobur Rahman

    (Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
    These authors contributed equally to this work.)

  • Rolando Vega

    (Texas Sustainable Energy Research Institute, San Antonio, TX 78249, USA)

  • Bing Dong

    (Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

Abstract

We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.

Suggested Citation

  • Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:55-:d:62434
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    References listed on IDEAS

    as
    1. Benghanem, Mohamed & Mellit, Adel, 2010. "Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia," Energy, Elsevier, vol. 35(9), pages 3751-3762.
    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. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
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