AI Lund lunch seminar: Physics-informed learning for identification of a residential building's thermal behavior
Contact: jonas [dot] Wisbrant [at] cs [dot] lth [dot] se
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Sound track and slide deck are available at ai.lu.se
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.