Guanjing Lin is an associate professor and doctoral supervisor at  Tsinghua Shenzhen International Graduate School.  She received her bachelor's degree and master's degree from Tsinghua University, and received doctor's degree from Texas A&M University, USA. She was a principal investigator at Lawrence Berkeley National Laboratory from 2013 to 2022.

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.

Representative Papers

[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. DOI: https://doi.org/10.3390/en15134858

[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.

Software Copyright

[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