Energies 2013, 6(4), 1918-1929; doi:10.3390/en6041918

Hybrid Predictive Models for Accurate Forecasting in PV Systems

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Received: 9 January 2013; in revised form: 8 February 2013 / Accepted: 26 February 2013 / Published: 3 April 2013
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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.
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.

AMA Style

Ogliari E, Grimaccia F, Leva S, Mussetta M. Hybrid Predictive Models for Accurate Forecasting in PV Systems. Energies. 2013; 6(4):1918-1929.

Chicago/Turabian Style

Ogliari, Emanuele; Grimaccia, Francesco; Leva, Sonia; Mussetta, Marco. 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems." Energies 6, no. 4: 1918-1929.

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