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

Alfredo Nespoli, Emanuele Ogliari, Sonia Leva, Alessandro Massi Pavan, Adel Mellit, Vanni Lughi and Alberto Dolara
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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

Energies, 2019, vol. 12, issue 9, 1-15

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.

Keywords: neural networks; day-ahead forecasting; PV system; micro-grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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