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@ARTICLE{,
    author = {Jara, Antonio J and Dufour, Luc and Rizzo, Gianluca and Genoud, Dominique and Cotting, Alexandre and Bocchi, Yann and Chabbey, Fran{\c c}ois},
  keywords = {Advanced Metering Infrastructure, Data intelligence analysis, Energy information management, Internet of Things, Microgrid, Smart Grid},
     month = jan,
     title = {I-BAT : A Data-intensive Solution based on the Internet of Things to Predict Energy Behaviours in Microgrids},
   journal = {International Journal of Data Warehousing and Mining (IJDWM)},
      year = {2014},
  abstract = {Microgrids present the challenge to reach a proper balance between local
production and consumption, in order to reduce the usage of energy from external
sources. This work presents a data-intensive solution to predict the energy
behaviours. Thereby, control actions can be carried out such as decrease heating
systems levels and switch o low-priority devices. For this purpose, this work
has deployed an Advanced Metering Infrastructure (AMI) based on the Internet
of Things (IoT) in the Techno-Pole testbed. This deployment provides the data
from energy-related parameters such as load curves of the overall building through
Non-Intrusive Load Monitoring (NILM), a wireless network of IoT-based smart
meters to measure and control appliances, and nally the generated power curve
by 2000 square meters of photovoltaic panels. The prediction model proposed
is based on recognition of electrical signatures. These electrical signatures have
been used to detect complex usage patterns. The modelled patterns has allowed to
identify the work day of the week, and predict the load and generation curves for
15 minutes with an accuracy over the 90\%. This short-term prediction is allowing
us to carry out the proper actions in order to balance the microgrid status (i.e.,
get a proper balance between production and consumption).}
}