%Aigaion2 BibTeX export von HES SO Valais Publications
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@ARTICLE{,
    author = {Barque, Mariam and Dufour, Luc and Genoud, Dominique and Zufferey, Arnaud and Ladevie, Bruno and Bezian, Jean-Jacques},
  keywords = {Advanced Metering Infrastructure, Data intelligence analysis, Energy information management, KNIME, Microgrid, PV forecast, Solar production prediction},
     month = jul,
     title = {Solar production prediction based on non linear meteo source adaptation},
   journal = {IEE},
      year = {2015},
     pages = {5},
  abstract = {This work presents a data-intensive solution to
predict Photovolta¨{\i}que energy (PV) production. PV and other
renewable sources have widely spread in recent years. Although
those sources provide an environmentally-friendly solution, their
integration is a real challenge in terms of power management as
it depends on meteorological conditions. The ability to predict
those variable sources considering meteorological uncertainty
plays a key role in the management of the energy supply needs
and reserves. This paper presents an easy-to-use methodology to
predict PV production using time series analyses and sampling
algorithms. The aim is to provide a forecasting model to set the
day-ahead grid electricity need. This information useful for power
dispatching plans and grid charge control. The main novelties of
our approach is to provide an easy implemented and flexible
solution that combines classification algorithms to predict the
PV plant efficiency considering weather conditions and nonlinear
regression to predict weather forecasted errors in order to
improve prediction results. The results are based on the data
collected in the Techno-poles microgrid in Sierre (Switzerland)
described further in the paper. The best experimental results
have been obtained using hourly historical weather measures
(radiation and temperature) and PV production as training
inputs and weather forecasted parameters as prediction inputs.
Considering a 10 month dataset and despite the presence of 17
missing days, we achieve a Percentage Mean Absolute Deviation
(PMAD) of 20\% in August and 21\% in September. Better results
can be obtained with a larger dataset but as more historical data
were not available, other months have not been tested.}
}