Title: Machine Learning for Smart Buildings: Applications and Perspectives
Abstract: Fueled by big data, powerful computing, and advanced algorithms, machine learning has been explored and applied to smart buildings and has demonstrated its potential to enhance building performance. This talk presents an overview of how machine learning has been applied across different stages of building life cycle with a focus on building design, operation, and control. A few applications using machine learning will be presented. Challenges and opportunities of applying machine learning to buildings research will be discussed also.
Bio: Dr. Tianzhen Hong is a Senior Scientist and Deputy Head of the Building Technologies Department of LBNL. He leads the Urban Systems Group and a team with research on data, methods, computing, occupant behavior, and policy for design and operation of low energy buildings and sustainable urban systems. He is an IBPSA Fellow and ASHRAE Fellow. He received B.Eng. and Ph.D. in HVACR, and B.Sc. in Applied Mathematics from Tsinghua University, China.