Research dealing with renewable energy sources is focusing on the possibility to forecast the daily output power profile of a given power system. In particular, photovoltaic has gained more interest because it is relatively easy to install but the power output shows high level of variability, strongly depending on meteorological conditions. The present work is devoted to improve the accuracy of the photovoltaic power prediction. For this reason an Hybrid Artificial Neural Network based system is used to the scope and in order to reduce the difference between the forecast and the daily measured power profile, a novel criterion based on night hours forecast is proposed. The implemented methodologies have been applied and validated throughout the work, emphasizing their partial contribution. The simulations and results are obtained on the basis of a photovoltaic module recordings of the year 2017 in Solar Tech Lab in Milano, Italy. Weather forecasts and real hourly photovoltaic power data are made available to the readers.

PV power forecasting improvement by means of a selective ensemble approach

Leva S.;Mussetta M.;Nespoli A.;Ogliari E.
2019

Abstract

Research dealing with renewable energy sources is focusing on the possibility to forecast the daily output power profile of a given power system. In particular, photovoltaic has gained more interest because it is relatively easy to install but the power output shows high level of variability, strongly depending on meteorological conditions. The present work is devoted to improve the accuracy of the photovoltaic power prediction. For this reason an Hybrid Artificial Neural Network based system is used to the scope and in order to reduce the difference between the forecast and the daily measured power profile, a novel criterion based on night hours forecast is proposed. The implemented methodologies have been applied and validated throughout the work, emphasizing their partial contribution. The simulations and results are obtained on the basis of a photovoltaic module recordings of the year 2017 in Solar Tech Lab in Milano, Italy. Weather forecasts and real hourly photovoltaic power data are made available to the readers.
2019 IEEE Milan PowerTech, PowerTech 2019
978-1-5386-4722-6
ANN sizing; Artificial neural network; Ensemble method; PV power forecasting; Renewable sources; Short-term
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1120801
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