Linguanjing

Sep. 2006–Dec. 2012, Ph.D. in Mechanical Engineering, Texas A&M University, United States

Sep. 2004–Jun. 2006, Master in Building Science and Technology, Tsinghua University, China

Sep. 2004–Jun. 2006, Bachelor in Building Science and Technology, Tsinghua University, China

Dec. 2022 - present, Associate Professor, Tsinghua Shenzhen International Graduate School, China

Jan. 2019 – Nov. 2022, Principal Scientific Engineering Associate, Lawrence Berkeley National Laboratory, United States

Mar. 2013 – Dec. 2018, Senior Scientific Engineering Associate, Lawrence Berkeley National Laboratory, United States

Member of American Society of Heating, Refrigerating, and Air-Conditioning Engineers, TC 7.5 committee member

Guanjing Lin’s research focus on smart building energy management and information system. Her research interests include

-          HVAC fault detection, diagnostics and correction

-          Building optimal control

-          Building commissioning, operation and maintenance

-          Digital twin, big data application

-           

Guanjing Lin joined Tsinghua Shenzhen International Graduate School as an Associate Professor in December 2022. She worked in Lawrence Berkeley National Laboratory, United States from March 2013 to 2022. As the project PI, Co-PI or technical lead, she has completed 14 research projects funded by U. S. Department of Energy. She developed a performance evaluation framework for building fault detection and diagnosis algorithms; curate, validate, and publish the world’s largest set of labeled time-series data representing commercial HVAC systems operating in faulted and fault-free states; and developed corrective logic for the most common and most readily addressable controls problems.  Her research has got four software copyright certificates and was also licensed to private companies.

[1]    Lin G*, Pritoni M, Chen Y, Vitti R, Weyandt C, Granderson J. Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics Tool. Energy and Buildings. 2023 283: 112796.

[2]    Lin G, Kramer H*, Nibler V, Crowe E, Granderson J*. Building analytics tool deployment across thousands of United States buildings: benefits, costs, and the state of practice. Energies. 2022 15(13), p.4858.

[3]    Chen Y*, Lin G, Chen Z, Wen J, Granderson J. A simulation-based evaluation of fan coil unit fault effects. Energy and Buildings. 2022 263:112041.

[4]    Pritoni M, Lin G*, Chen Y, Vitti R, Weyandt C, Granderson J. From fault-detection to automated fault correction: A field study. Building and Environment. 2022 214:108900.

[5]    Chen Y, Lin G, Crowe E*, Granderson J. Development of a unified taxonomy for HVAC system faults. Energies, 2021 14(17), p.5581.

[6]    Granderson J*, Lin G*, Harding A, Im P, Chen Y. Building fault detection data to aid diagnostic algorithm creation and performance testing. Nature Scientific Data. 2020 Feb 24;7(1):1-4.

[7]    Lin G, Pritoni M, Chen Y, Granderson J*. Development and implementation of fault-correction algorithms in fault detection and diagnostics tools. Energies. 2020 Jan;13(10):2598.

[8]    Lin G, Kramer H, Granderson J*. Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance. Building and Environment. 2020 (168):106-505.

[9]    Granderson J*, Lin G, Blum D, Page J, Spears M, Piette MA. Integrating diagnostics and model-based optimization. Energy and Buildings. 2019 (182):187-95.

[10]Frank S, Lin G, Jin X, Singla R, Farthing A, Granderson J*. A performance evaluation framework for building fault detection and diagnosis algorithms. Energy and Buildings. 2019 (192):84-92.

[11]Kramer H, Lin G, Curtin C, Crowe E, Granderson J*. Building analytics and monitoring-based commissioning: industry practice, costs, and savings. Energy Efficiency. 2019:1-3.

[12]Granderson J, Lin G*, Singla R, Fernandes S, Touzani S. Field evaluation of performance of HVAC optimization system in commercial buildings. Energy and Buildings. 2018 (173):577-86.

[13]Granderson J*, Lin G. Building energy information systems: Synthesis of costs, savings, and best-practice uses. Energy Efficiency. 2016 9(6):1369-84.

[14]Lin G, Claridge DE*. A temperature-based approach to detect abnormal building energy consumption. Energy and Buildings. 2015 (93):110-8.

Software License:

[1]    Vitti R, Weyandt C, Lin G, Pritoni M, Yimin C，Granderson J, Haxall-based (Axon) fault auto-correction package for building HVAC systems, 2022

[2]    Yimin C, Lin G, Najibi R, Fernandes S, Retro-Commissioning Sensor Suitcase Plus, 2022

[3]    Granderson J, Hu L, Blum D, Spears M, Bonvini M, PlantInsight v1: Modelica model-based optimization and fault diagnostics for central cooling plants, 2018

[4]     McQuillen D, Mitchell R, Lin G, Granderson J, Retro-Commissioning Sensor Suitcase, 2017

“Lawrence Berkeley National Laboratory Outstanding Performance”, 2018, 2019, 2020, 2022

“Federal Laboratory Consortium Award for Excellence in Technology Transfer”, 2020

“Lawrence Berkeley National Laboratory Director’s Award for Exceptional Achievement - Tech Transfer, 2017