  In this paper, an energy conservation measure that optimizes the planning of
heating and cooling systems for tertiary sector buildings is proposed. It
consists of a model-based predictive control approach that employs a grey-box
model built from the building data history and from weather condition data that
predicts the building heat load and indoor temperature. This model is then used
by heating and cooling optimization strategies that aim at reducing the total
energy consumption of the building in the next day while satisfying the desired
indoor thermal comfort constraint. The proposed optimization strategies do not
modify the regulation mode in place; rather, they send optimized set-points to
the building management system in order to reduce the energy consumption. We
applied our approach in a case study of a commercial building during heating
and cooling seasons and we show that it was able to yield up to 12% of energy
savings while having a mean power forecast error of 8%.
