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Hybrid Predictive Models for Accurate Forecasting in PV Systems
Department of Energy, Polytechnic University of Milan, Via La Masa 34, I-20156 Milano, Italy
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Received: 9 January 2013; in revised form: 8 February 2013 / Accepted: 26 February 2013 / Published: 3 April 2013
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
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MDPI and ACS Style
Ogliari, E.; Grimaccia, F.; Leva, S.; Mussetta, M. Hybrid Predictive Models for Accurate Forecasting in PV Systems. Energies 2013, 6, 1918-1929.
Ogliari E, Grimaccia F, Leva S, Mussetta M. Hybrid Predictive Models for Accurate Forecasting in PV Systems. Energies. 2013; 6(4):1918-1929.
Ogliari, Emanuele; Grimaccia, Francesco; Leva, Sonia; Mussetta, Marco. 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems." Energies 6, no. 4: 1918-1929.