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
Article Menu

Export Article

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

Hybrid Predictive Models for Accurate Forecasting in PV Systems

Department of Energy, Polytechnic University of Milan, Via La Masa 34, I-20156 Milano, Italy
Authors to whom correspondence should be addressed.
Received: 9 January 2013 / Revised: 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)
View Full-Text   |   Download PDF [1459 KB, uploaded 17 March 2015]   |  


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. View Full-Text
Keywords: hybrid techniques; PV forecasting; artificial Intelligence; neural networks hybrid techniques; PV forecasting; artificial Intelligence; neural networks

Figure 1

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top