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@ARTICLE{,
    author = {Dufour, Luc and Genoud, Dominique and Genoud, Stephane and Cimmino, Francesco Maria and Ladevie, Bruno and Bezian, Jean-Jacques},
     title = {Economic interest of heating and hot water 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 which used
supervised learning techniques. This paper presents an industrial
methodology to predict heating and hot water consumption using
time series analyzes and tree ensemble algorithm.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.The results are based on the data collected in
a building in Chamoson (Switzerland) and simulations. The aim
is to provide to the virtual power plant the possibility to pilot an
part of energy consumption. The input data for the pilot is the
economic parameter. Considering the economic input data for the
energy management, a new heasting and hot water consumption
is provided for one week.},
Heating consumption prediction; Hot water consumption
prediction; Data intelligence analysis; Energy Price;
Storage; Microgrid; Predictive energy management; KNIME;
}