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SUMMARY:Data-driven Building Energy Systems: Applications\, Platforms\, an
 d Benchmarking - Dan Wang - The Hong Kong Polytechnic University
DTSTART:20230630T120000Z
DTEND:20230630T130000Z
UID:TALK202123@talks.cam.ac.uk
CONTACT:114742
DESCRIPTION:The traditional way to control energy systems is by strategies
  based on principles of physics. Now there is a transformation where the d
 ecision-making is driven by information and data technologies. In this tal
 k\, we will briefly review some data-driven applications. With an increasi
 ng number of applications\, a new challenge is to support such application
 s at scale. The main problem is how to handle various levels of heterogene
 ities in different building energy systems so that data and machine learni
 ng models can be automatically handled by a platform with minimal human in
 volvement. We present our recent works to automatically translate data int
 o standards and extract data under various local data conventions\; as wel
 l as schemes to evaluate appropriate machine learning models at scale. We 
 are working with the Electrical and Mechanical Services Department (EMSD) 
 of the Hong Kong government to benchmark data and machine learning models\
 , in the hope to accelerate AI deployment in the campaign towards a smart 
 city and a green city.\n\nDan Wang is a professor in the Department of Com
 puting\, The Hong Kong Polytechnic University. His research interests lie 
 in smart energy systems. He published in ACM eEnergy and ACM Buildsys\, an
 d he won the best papers in both conferences. He is currently the steering
  committee chair of ACM eEnergy. He is an advisor of EMSD\, the Hong Kong 
 SAR government. He has extensive experience in applied research and his re
 search results have been adopted by industry\, including Huawei\, IBM\, He
 nderson\, etc.
LOCATION:FW 11\, William Gates Building. Zoom link: https://cl-cam-ac-uk.z
 oom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&amp\;from=addon 
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