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Appl. Sci. 2017, 7(6), 622;

ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant

Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Author to whom correspondence should be addressed.
Academic Editor: Allen Barnett
Received: 4 May 2017 / Revised: 5 June 2017 / Accepted: 12 June 2017 / Published: 15 June 2017
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance. View Full-Text
Keywords: artificial neural network; day-ahead forecast; ensemble methods artificial neural network; day-ahead forecast; ensemble methods

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Grimaccia, F.; Leva, S.; Mussetta, M.; Ogliari, E. ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant. Appl. Sci. 2017, 7, 622.

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