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N. Sterl:
"Predictive Thermal Control with different sensor configurations";
Supervisor: A. Mahdavi, M. Schuss; Institut für Architekturwissenschaften, Abteilung Bauphysik und Bauökologie, 2015; final examination: 2015-11-25.

Saving energy in general and heating energy in particular has always been in the focus of research and development. Progress of technology, the potential of modern electronics, and advances in the digital world have led to many new practical opportunities to reduce the heating demand of buildings.
In this context, one field of research concentrates on state-of-the-art control strategies.
Electronics and microprocessors have become very powerful and reasonably priced and applications as embedded control have taken an important role in our life. This technology has also entered the thermal control of buildings, homes and apartments.
'Smart' thermostats are a fast growing market and attract start-ups as well as big players in computer engineering. The applied technologies follow different paths to achieve energy savings.
Use of advanced control systems is an important direction of research. And there, control algorithms using predictive control algorithms are one of these potential ways and are showing promising results. These prediction algorithms allow for optimized control strategies, taking into account a wide range of input parameters. The basis for the prediction and optimization process is a good knowledge about the systems characteristics, either by an empirically established set of system responses or by a mathematical model representing the thermal dynamics of the controlled zone. Both ways allow forecasting the thermal systems output as a reaction to an applied control input and to disturbance parameters.
For a selected actual room, different predictive types of controllers and their respective energy saving potential is discussed. Switching thermostats with simple predictive methods as well as more complex model predictive control algorithm are compared. Also variations of sensor and input data configurations are compared in view of the achieved simulated energy savings.

Saving energy in general and heating energy in particular has always been in the focus of research and development. Progress of technology, the potential of modern electronics, and advances in the digital world have led to many new practical opportunities to reduce the heating demand of buildings.
In this context, one field of research concentrates on state-of-the-art control strategies.
Electronics and microprocessors have become very powerful and reasonably priced and applications as embedded control have taken an important role in our life. This technology has also entered the thermal control of buildings, homes and apartments.
'Smart' thermostats are a fast growing market and attract start-ups as well as big players in computer engineering. The applied technologies follow different paths to achieve energy savings.
Use of advanced control systems is an important direction of research. And there, control algorithms using predictive control algorithms are one of these potential ways and are showing promising results. These prediction algorithms allow for optimized control strategies, taking into account a wide range of input parameters. The basis for the prediction and optimization process is a good knowledge about the systems characteristics, either by an empirically established set of system responses or by a mathematical model representing the thermal dynamics of the controlled zone. Both ways allow forecasting the thermal systems output as a reaction to an applied control input and to disturbance parameters.
For a selected actual room, different predictive types of controllers and their respective energy saving potential is discussed. Switching thermostats with simple predictive methods as well as more complex model predictive control algorithm are compared. Also variations of sensor and input data configurations are compared in view of the achieved simulated energy savings.

thermal building/room model, grey-box room model identification, predictive switching thermostat, model predictive thermostat control, smart thermostat