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M. Killian, A. Leitner, R. Goldgruber, M. Kozek:
"Adaptive model predictive control for energy-efficient smart homes";
Talk: 7th International Symposium on Energy, Manchester, UK (invited); 2017-08-13 - 2017-08-17; in: "Proceedings of 7th International Symposium on Energy", (2017).

The building sector accounts for 20 % to 40 % of the energy consumption in developed countries
including both residential and non-residential buildings, where one-third of this consumption
depends on heating and cooling a building. Thus, there has been a growing rethinking in energy
savings and the efficient usage of energy in buildings has become an important current task. Two
main but conflicting optimization goals occur in buildings: 1) maximization of user comfort, and 2)
minimization of energy consumption. Model predictive control (MPC) optimally utilizes predictions
of future disturbances (ambient temperature, solar radiation, occupancy), and it is the ideal control
strategy to deal with conflicting optimization goals. Furthermore, MPC is perfectly suited for
including thermal and technical constraints in the optimization statement, considering large time
constants (due to thermal mass and good insulation), and decoupling of multi-variate control
problems, thus rendering MPC a powerful control scheme.
Structural modifications are not needed to reduce energy consumption and CO2-emission for heating
and cooling in existing and new buildings with an intelligent energy-efficient home automation
system (energy reduction up to 40 %). In this work an intelligent adaptive MPC for home automation
is introduced. To get the highest possible performance with the least effort, beside the MPC concept,
a self-adaptive model for buildings and user-behaviour is proposed. This self-adaptive model is able
to correct model-errors because of adapting the user-behaviour. Moreover the model is able to
manage a new parametrization during on-line operation. Furthermore another key-aspect is the
choice of a suitable data mining and processing algorithm.
By reason of adaptive MPC the thermal comfort in the building increases while energy consumption
is reduced effectively. Furthermore a flexible use of green energy is easy realisable. Because of the
smart usability with smart phones or tablets, the system is easy and clear to handle and the concept is
user-friendly as well. Moreover, the opportunity for smart grids is given.

smart home, optimizing thermal comfort, model predictive control (MPC), adaptive MPC, adaptive clustering, building automation, energy-efficience