Physics-informed learning for identification of a residential building's thermal behavior
Recording from AI Lund lunch seminar 1 December 2021
Title: Physics-informed learning for identification of a residential building's thermal behavior
When: 1 December 2021, 12:00-13:15
As space heating represents a large share of total energy use, thermal networks, i.e. district cooling or heating networks, would be able to increase the efficiency of the energy system in an economic way. Thanks to the natural inertia of heat exchanges, these networks can offer flexibility. In order to explore this feature, it is important to model building's thermal behavior in order to enable the use of demand-side management control strategies. In this work, such models are built through a physics-informed learning based approach, taking advantage of the available measurements.