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A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Author

Listed:
  • Alberto Dolara

    (Department of Energy, Politecnico di Milano, Milano 20133, Italy
    These authors contributed equally to this work.)

  • Francesco Grimaccia

    (Department of Energy, Politecnico di Milano, Milano 20133, Italy
    These authors contributed equally to this work.)

  • Sonia Leva

    (Department of Energy, Politecnico di Milano, Milano 20133, Italy
    These authors contributed equally to this work.)

  • Marco Mussetta

    (Department of Energy, Politecnico di Milano, Milano 20133, Italy
    These authors contributed equally to this work.)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, Milano 20133, Italy
    These authors contributed equally to this work.)

Abstract

The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:1138-1153:d:45428
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    References listed on IDEAS

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    3. 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.
    4. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    5. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
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