Résumé:
We aim at the elaboration of Information Systems able to optimize energy consumption in
buildings while preserving human comfort. Our focus is in the use of state-based stochas-
tic modeling applied to temporal signals acquired from heterogeneous sources such as
distributed sensors, weather web services, calendar information and user triggered events.
Our general scienti_c objectives are: (1) global instead of local optimization of building
automation sub-systems (heating, ventilation, cooling, solar shadings, electric lightings),
(2) generalization to unseen building con_guration or usage through self-learning data-
driven algorithms and (3) inclusion of stochastic state-based modeling to better cope
with seasonal and building activity patterns. We leverage on state-based models such as
Hidden Markov Models (HMMs) to be able to capture the spatial (states) and temporal
(sequence of states) characteristics of the signals. We envision several application layers
as per the intrinsic nature of the signals to be modeled. We also envision room-level
systems able to leverage on a set of distributed sensors (temperature, presence, electricity
consumption, etc.). A typical example of room-level system is to infer room occupancy
information or activities done in the rooms as a function of time. Finally, building-level
systems can be composed to infer global usage and to propose optimization strategies for
the building as a whole. In our approach, each layer may be fed by the output of the
previous layers.
More speci_cally in this paper, we report on the design, conception and validation of
several machine learning applications. We present three di_erent applications of state-
based modeling. In the _rst case we report on the identi_cation of consumer appliances
through an analysis of their electric loads. In the second case we perform the activity
recognition task, representing human activities through state-based models. The third
case concerns the season prediction using building data, building characteristic parameters
and meteorological data.
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