Next Article in Journal
High Pressure Oxydesulphurisation of Coal—A Parametric Study
Next Article in Special Issue
Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings
Previous Article in Journal
Application of Coordinated SOFC and SMES Robust Control for Stabilizing Tie-Line Power
Previous Article in Special Issue
Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting
Energies 2013, 6(4), 1918-1929; doi:10.3390/en6041918
Article

Hybrid Predictive Models for Accurate Forecasting in PV Systems

, * ,
 and *
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)
Download PDF [1459 KB, uploaded 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 hybrid techniques; PV forecasting; artificial Intelligence; neural networks
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.

Export to BibTeX |
EndNote


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.


Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert