Hybrid Predictive Models for Accurate Forecasting in PV Systems

Emanuele Ogliari, Francesco Grimaccia, Sonia Leva and Marco Mussetta
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Emanuele Ogliari: Department of Energy, Polytechnic University of Milan, Via La Masa 34, I-20156 Milano, Italy
Francesco Grimaccia: Department of Energy, Polytechnic University of Milan, Via La Masa 34, I-20156 Milano, Italy
Sonia Leva: Department of Energy, Polytechnic University of Milan, Via La Masa 34, I-20156 Milano, Italy
Marco Mussetta: Department of Energy, Polytechnic University of Milan, Via La Masa 34, I-20156 Milano, Italy

Energies, 2013, vol. 6, issue 4, 1-12

Abstract: The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error.

Keywords: hybrid techniques; PV forecasting; artificial Intelligence; neural networks (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: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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