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A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

Author:
Almonacid, F., Pérez-Higueras, P.J., Fernández, Eduardo F., Hontoria, L.
Source:
Energy conversion and management 2014 v.85 pp. 389-398
ISSN:
0196-8904
Subject:
methodology, neural networks, solar collectors, solar energy, weather
Abstract:
One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy.
Agid:
6002448