BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Next-Gen Building Energy Modeling: How AI Fuels Digital Twin Solut
 ions - Jie Lu\, Zhejiang University
DTSTART:20241121T120000Z
DTEND:20241121T130000Z
UID:TALK224524@talks.cam.ac.uk
CONTACT:Monty Jackson
DESCRIPTION:Talk Title:\n*Next-Gen Building Energy Modeling: How AI Fuels 
 Digital Twin Solutions*\n \nSpeaker:\n*Jie Lu*\n \nSpeaker Bio:\nJie Lu is
  a visiting Ph.D. student in the Energy Efficient Cities Initiative (EECi)
 \, at the University of Cambridge's Department of Engineering. Her backgro
 und is in Heating and Ventilation\, with a bachelor's degree from Nanjing 
 Normal University (NNU)\, and a master's degree from Zhejiang University (
 ZJU). She is currently a PhD candidate in Power Engineering and Engineerin
 g Thermophysics at ZJU\, China\, with a research focus on the modelling of
  digital twins in building energy systems.\nJie’s primary work includes 
 developing effective and efficient hybrid modelling methods\, such as nove
 l approaches for first-principle models and machine learning techniques fo
 r load estimation. Additionally\, she focuses on retrofitting green buildi
 ngs to enhance their operational flexibility. Her research contributes to 
 advancing sustainable and resilient building practices\, integrating techn
 ical innovation with a strong emphasis on energy efficiency in urban envir
 onments.\n \n \nTalk Abstract:\nThis presentation explores the transformat
 ive potential of advanced AI technologies—such as large language models 
 (LLMs)\, graph neural networks (GNNs)\, and variational autoencoders (VAEs
 )—in digital twin modelling for building energy systems. With LLMs servi
 ng as the “intelligence” behind our models\, we streamline automated b
 uilding simulations from geometry extraction to parameter calibration. Yet
 \, challenges like incomplete data\, design load uncertainties\, and the n
 eed for scalable data-driven solutions remain. To address these\, we integ
 rate GNNs to handle load estimation uncertainties and VAEs to impute missi
 ng parameters. This layered approach empowers the LLM-driven digital twin 
 to more accurately replicate and optimise complex building environments\, 
 setting the stage for smarter\, more sustainable energy systems.\n \n-----
 -----\n \nJoin Zoom Meeting\nhttps://cam-ac-uk.zoom.us/j/88230088472?pwd=d
 XhkeWJVL3lHVGRERUtwL3BOK1dPUT09\n\nMeeting ID: 882 3008 8472\nPasscode: 68
 5305\n\n----------\n\nFor further information and to RSVP\, please contact
  Monty Jackson (mj636@cam.ac.uk). 
LOCATION:Seminar Room\, 2nd Floor South Wing (2S)\, Roger Needham Building
 \, University of Cambridge
END:VEVENT
END:VCALENDAR