Automatic thermal model identification and distributed optimization for load shifting in city quarters

Modern buildings with floor heating or thermally activated building structures (TABS) offer a significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realized with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models describing the thermal characteristics of the individual thermal zones exist. Creating these by hand or from more detailed simulation models is infeasible for large numbers of zones; instead, they must be identified automatically based on measurement data. We present an approach using only open source tools based on the programming language Julia that allows to robustly identify simple thermal models for heating and cooling usable in MPC optimization. The resulting models are used in a distributed optimization scheme that co-ordinates the individual zones and buildings of a city quarter in order to best support an energy hub.