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

Alberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta and Emanuele Ogliari
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Alberto Dolara: Department of Energy, Politecnico di Milano, Milano 20133, Italy
Francesco Grimaccia: Department of Energy, Politecnico di Milano, Milano 20133, Italy
Sonia Leva: Department of Energy, Politecnico di Milano, Milano 20133, Italy
Marco Mussetta: Department of Energy, Politecnico di Milano, Milano 20133, Italy
Emanuele Ogliari: Department of Energy, Politecnico di Milano, Milano 20133, Italy

Energies, 2015, vol. 8, issue 2, 1-16

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.

Keywords: Artificial Neural Network (ANN); energy forecasting; renewable energy source (RES) integration (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: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (54)

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