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Energies 2015, 8(2), 1138-1153; doi:10.3390/en8021138

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Department of Energy, Politecnico di Milano, Milano 20133, Italy
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Jean-Michel Nunzi
Received: 19 November 2014 / Revised: 22 December 2014 / Accepted: 23 January 2015 / Published: 3 February 2015
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Abstract

The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications. View Full-Text
Keywords: Artificial Neural Network (ANN); energy forecasting; renewable energy source (RES) integration Artificial Neural Network (ANN); energy forecasting; renewable energy source (RES) integration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dolara, A.; Grimaccia, F.; Leva, S.; Mussetta, M.; Ogliari, E. A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output. Energies 2015, 8, 1138-1153.

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