A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power OutputReport as inadecuate




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Department of Energy, Politecnico di Milano, Milano 20133, Italy



These authors contributed equally to this work.





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Academic Editor: Jean-Michel Nunzi

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





Author: Alberto Dolara †, Francesco Grimaccia †, Sonia Leva †, Marco Mussetta †,* and Emanuele Ogliari †

Source: http://mdpi.com/



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