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Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Author

Listed:
  • Alfredo Nespoli

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Sonia Leva

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Alessandro Massi Pavan

    (Department of Engineering and Architecture, Università degli Studi di Trieste, 34127 Trieste, Italy)

  • Adel Mellit

    (Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria)

  • Vanni Lughi

    (Department of Engineering and Architecture, Università degli Studi di Trieste, 34127 Trieste, Italy)

  • Alberto Dolara

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

Abstract

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

Suggested Citation

  • Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1621-:d:226792
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    References listed on IDEAS

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    1. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    3. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    4. Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
    5. 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.
    6. d'Alessandro, Vincenzo & Di Napoli, Fabio & Guerriero, Pierluigi & Daliento, Santolo, 2015. "An automated high-granularity tool for a fast evaluation of the yield of PV plants accounting for shading effects," Renewable Energy, Elsevier, vol. 83(C), pages 294-304.
    7. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    8. 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.
    9. Yu. A. Ul’yanin & V. V. Kharitonov & D. Yu. Yurshina, 2018. "Forecasting the Dynamics of the Depletion of Conventional Energy Resources," Studies on Russian Economic Development, Springer, vol. 29(2), pages 153-160, March.
    10. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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