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SUMMARY:Modelling Building Thermal Dynamics – From Data Generation to Tr
 ansfer Learning - Fabian Raisch\, Technical University of Munich
DTSTART:20250515T120000Z
DTEND:20250515T130000Z
UID:TALK231835@talks.cam.ac.uk
CONTACT:114742
DESCRIPTION:*Abstract*\n\nBuilding operations contribute approximately one
 -third of global CO₂ emissions. Advanced control strategies can reduce t
 hese emissions by up to 30%. Such control requires accurate mathematical m
 odels that capture the building’s thermal dynamics. Data-driven modeling
  has emerged as the most scalable approach for this purpose. However\, the
  availability of high-quality building data remains limited. To address th
 is challenge\, we propose two methods: (1) a data generation framework tha
 t synthesizes realistic building operation data\, and (2) a general Transf
 er Learning model that serves as an effective initialization for modeling 
 new target buildings.\n\n*Bio*\n\nFabian is a second-year PhD student in t
 he Department of Energy Management Technologies at the Technical Universit
 y of Munich\, supervised by Prof. Dr. Christoph Goebel. His research focus
 es on using Machine Learning to model building thermal dynamics. Such mode
 ls are necessary for enabling Model Predictive Control of the building\, w
 hich can reduce CO₂ emissions by up to 30%.
LOCATION:Room GS15 at the William Gates Building and on Zoom: https://cl-c
 am-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=ad
 don 
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