Guanjing Lin is a Principle Scientific Engineering Associate in the Building Technology and Urban Systems Division at LBNL. Her research interests include building energy performance monitoring, fault detection and diagnostic, and building operation and commissioning. She received her PhD in Mechanical Engineering from Texas A&M University, and her MS and BS in Building Science from Tsinghua University, China.
Retro-commissioning Sensor Suitcase Commercialization Team: Samuel Fernandes, Jessica Granderson, Guanjing Lin, Robin Mitchell
"Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance." Building and Environment 168 (2020) 106505. .
"Building fault detection data to aid diagnostic algorithm creation and performance testing." Nature: Scientific Data Vol.7 No.65 (2020). .
"A performance evaluation framework for building fault detection and diagnosis algorithms." Energy and Buildings 192 (2019) 84 - 92. .
"Building analytics and monitoring-based commissioning: industry practice, costs, and savings." Energy Efficiency (2019). .
"Integrating diagnostics and model-based optimization." Energy and Buildings 182 (2019) 187 - 195. .
"Commercial Fault Detection and Diagnostics Tools: What They Offer, How They Differ, and What’s Still Needed." 2018 ACEEE Summer Study on Energy Efficiency in Buildings. 2018. .
"Field evaluation of performance of HVAC optimization system in commercial buildings." Energy and Buildings 173 (2018) 577 - 586. .
"When Data Analytics Meet Site Operation: Benefits and Challenges." 2018 ACEEE Summer Study on Energy Efficiency in Buildings. 2018. .
"Building Energy Information Systems: Synthesis of Costs, Savings, and Best-practice Uses." Energy Efficiency (2016). LBNL-1006431. .
"Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?." Proceedings of the 2016 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, August 2016 (2016). LBNL-1006282. .
Energy information systems (EIS): Technology costs, benefit, and best practice uses. Berkeley, CA: Lawrence Berkeley National Laboratory, 2013. LBNL-6476E. .