Excellence in Research and Innovation for Humanity
} , ?>
@article{(International Science Index):http://waset.org/publications/8446,
  title    = {Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting},
  author    = {A. Chaouachi and  R.M. Kamel and  R. Ichikawa and  H. Hayashi and  K. Nagasaka},
  country   = {},
  institution={},
  abstract  = {This paper presents the applicability of artificial
neural networks for 24 hour ahead solar power generation forecasting
of a 20 kW photovoltaic system, the developed forecasting is suitable
for a reliable Microgrid energy management. In total four neural
networks were proposed, namely: multi-layred perceptron, radial
basis function, recurrent and a neural network ensemble consisting in
ensemble of bagged networks. Forecasting reliability of the proposed
neural networks was carried out in terms forecasting error
performance basing on statistical and graphical methods. The
experimental results showed that all the proposed networks achieved
an acceptable forecasting accuracy. In term of comparison the neural
network ensemble gives the highest precision forecasting comparing
to the conventional networks. In fact, each network of the ensemble
over-fits to some extent and leads to a diversity which enhances the
noise tolerance and the forecasting generalization performance
comparing to the conventional networks.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {3},
  number    = {6},
  year      = {2009},
  pages     = {1258 - 1263},
  ee        = {http://waset.org/publications/8446},
  url       = {http://waset.org/Publications?p=30},
  bibsource = {http://waset.org/Publications},
  issn      = {PISSN:2010-376X, EISSN:2010-3778},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 30, 2009},
}