Fueled by big data and powerful computing, advanced algorithms have been explored and limitedly applied to smart buildings and communities to enhance building performance (energy efficiency, energy flexibility, and climate resiliency). This talk presents how open data, data models, and data tools can be used to create datasets compliant with FAIR principles, and how such datasets together with metadata models and advanced algorithms can provide insights into building operations and controls to support building decarbonization at scale. A few examples from recent DOE-funded projects will be presented and discussed.
Presented by: IBPSA-USA Building Data Exchange (BDE) featuring Dr. Tianzhen Hong (Lawrence Berkeley National Laboratory), Dr. Na Luo (Lawrence Berkeley National Laboratory), and Han Li (Lawrence Berkeley National Laboratory)
Date: October 27, 2022
Dr. Tianzhen Hong
Dr. Tianzhen Hong is a Senior Scientist in the Building Technology and Urban Systems Division of LBNL. His research employs interdisciplinary approaches to explore technologies and human factors for planning, design and operation of energy-efficient, demand-flexible, and climate-resilient buildings across scales. He is an IBPSA Fellow, ASHRAE Fellow, and Highly Cited Researcher 2021.
Dr. Na Luo
Dr. Na Luo is a Senior Scientific Engineering Associate in the Building Technology and Urban Systems Division of LBNL. Her research focuses on building performance simulation, demand response modeling, building data curation and analytics, as well as urban microclimate CFD modeling.
Mr. Han Li
Mr. Han Li is a Senior Scientific Engineering Associate in the Building Technology and Urban Systems Division of LBNL. His current research focuses on the sensor data integration and machine learning. He is the lead developer of the BETTER tool for building energy benchmarking and retrofit targeting. He is a LEED-accredited professional.