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Article

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

1
Department of Energy, Politecnico di Milano, 20156 Milano, Italy
2
Department of Engineering and Architecture, Università degli Studi di Trieste, 34127 Trieste, Italy
3
Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria
*
Author to whom correspondence should be addressed.
Energies 2019, 12(9), 1621; https://doi.org/10.3390/en12091621
Received: 25 March 2019 / Revised: 12 April 2019 / Accepted: 23 April 2019 / Published: 29 April 2019
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
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. View Full-Text
Keywords: neural networks; day-ahead forecasting; PV system; micro-grid neural networks; day-ahead forecasting; PV system; micro-grid
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MDPI and ACS Style

Nespoli, A.; Ogliari, E.; Leva, S.; Massi Pavan, A.; Mellit, A.; Lughi, V.; Dolara, A. Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies 2019, 12, 1621. https://doi.org/10.3390/en12091621

AMA Style

Nespoli A, Ogliari E, Leva S, Massi Pavan A, Mellit A, Lughi V, Dolara A. Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies. 2019; 12(9):1621. https://doi.org/10.3390/en12091621

Chicago/Turabian Style

Nespoli, Alfredo, Emanuele Ogliari, Sonia Leva, Alessandro Massi Pavan, Adel Mellit, Vanni Lughi, and Alberto Dolara. 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques" Energies 12, no. 9: 1621. https://doi.org/10.3390/en12091621

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