%Aigaion2 BibTeX export from HES SO Valais Publications
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@ARTICLE{,
    author = {Dufour, Luc and Genoud, Dominique and Ladevie, Bruno and Bezian, Jean-Jacques},
  keywords = {Data intelligence analysis, Heating consumption prediction, Hot water consumption prediction, KNIME, Microgrid, Predictive energy management, Storage},
     title = {Heating and hot water industrial prediction system for residential district},
   journal = {IEEE AINA},
      year = {2016},
  abstract = {This work presents a data-intensive solution to
predict heating and hot water consumption. The ability to
predict locally those flexible sources considering meteorological
uncertainty can play a key role in the management of microgrid.
A microgrid is a building block of future smart grid, it can
be defined as a network of low voltage power generating units,
storage devices and loads.The main novelties of our approach is
to provide an easy implemented and flexible solution that used a
supervised learning techniques. This paper presents an industrial
methodology to predict heating and hot water consumption using
time series analyzes and tree ensemble algorithm.The results
are based on the data collected in a building in Chamoson
(Switzerland) and simulations. Considering the winter season
2012-2013 for the training,the heating and hot water predictions
is correctly estimated 90\% +/- 1.2 for the winter season 2013-
2014.}
}