"Machine Learning (ML) Based Data-driven Smart Building Applications and Challenges"
Dr. Jin Wen
Associate Dean for Research and Innovation and Professor, Department of Civil, Architectural and Environmental Engineering
Drexel University
Friday, October 4, 2024
12:00PM: Boxed lunches available
12:15PM - 1:15 PM: Presentation
Peter Kiewit Institute Room 160 (Omaha), Kiewit Hall A253 (Lincoln), or Zoom
Please use the form below to RSVP for lunch or the Zoom link by Tuesday, October 1, 2024 at 5 PM.
Machine Learning (ML) based methods have shown great potentials to provide cost-effective solutions for building performance improvement and building-grid integration. This seminar introduces the state-of-the-arts of typical ML based data-driven smart building applications, such as ML based model predictive control, fault detection and diagnosis, and occupant centric control. The seminar will discuss the gaps and challenges of applying ML based methods in real buildings, such as data quality and method scalability. The seminar will also provide an overview of International Energy Agency’s ANNEX 81 (Data-driven Smart Buildings) as well as other on-going projects that the speaker is engaged in.
Dr. Jin Wen is the Associate Dean for Research and Innovation of the College of Engineering, and a Professor in the Department of Civil, Architectural, and Environmental Engineering at Drexel University. She is a Fellow of American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE) and currently the Vice Chair of ASHRAE’s Research Administration Committee (RAC), which oversees and coordinates all ASHRAE research activities. She is the Task Leader for International Energy Agency (IEA)’s Energy in Buildings and Communities (EBC) Annex 81 (Data-Driven Smart Buildings) Task C (Applications). Dr. Wen was selected as the U.S. Fulbright Scholar for 2019-2020 (Sweden). Dr. Wen has more than twenty-years experiences in the smart building field, firstly as an application engineer working for Johnson Controls Inc. and later as a researcher in the areas of automated fault detection and diagnosis, building-to-grid integration, model predictive control, regional energy modeling, and occupant behavior simulation. Her work has been funded by the U.S. Department of Energy, the National Institute of Standard Technology, the National Science Foundation, the Department of Homeland Security, and ASHRAE. She has published 70+ journal papers and has 4800+ citation (Google Scholar).