Publications
The Berkeley Lab team’s full roster of publications may be accessed here, with selected publications provided below.
Lin, G, Kramer, H, Nibler, V, Crowe E, Granderson, J. 2022. Building analytics tool deployment at scale: Benefits, costs,and deployment practices. Energies 15(13). DOI: https://doi.org/10.3390/en15134858
Pritoni, M, Lin, G, Chen, Y, Vitti, R, Weyandt, C, Granderson, J. 2022. From fault-detection to automated fault correction: A field study. Building and Environment 214. DOI: https://doi.org/10.1016/j.buildenv.2022.108900
Kim, J, Trenbath, K, Granderson, J, Chen, Y, Crowe, E, Reeve, H, et al. 2021. Research challenges and directions in fault prevalence. Science and Technology for the Built Environment 27. DOI: https://doi.org/10.1080/23744731.2021.1898243
Lin, G, Pritoni, M, Chen, Y, Granderson, J. 2020. Development and implementation of fault-correction algorithms in fault detection and diagnostics tools. Energies 13(10) 2598. DOI: https://doi.org/10.3390/en13102598
Granderson, J, Lin, G, Harding, A, Im, P, Chen, Y. 2020. Building fault detection data to aid diagnostic algorithm creation and performance testing. Sci Data 7, 65 (2020). DOI: https://doi.org/10.1038/s41597-020-0398-6
Lin, G, Kramer, H, Granderson, J. 2020. Building fault detection and diagnostics: Achieved savings and methods to evaluate algorithm performance. Building and Environment 168. DOI: https://doi.org/10.1016/j.buildenv.2019.106505
Frank, S, Lin, G, Jin, X, Singla, R, Farthing, A, Granderson, J. 2018. A performance evaluation framework for building fault detection and diagnosis algorithms. Energy and Buildings 192:84-92. DOI: https://doi.org/10.1016/j.enbuild.2019.03.024
Granderson, J, Singla, R, Mayhorn, E, Ehrlich, P, Vrabie, D, Frank, S. 2017. Characterization and survey of automated fault detection and diagnostics tools. Lawrence Berkeley National Laboratory, Report Number LBNL-2001075. https://eta-publications.lbl.gov/sites/default/files/lbnl-2001075.pdf