0
  • DE
  • EN
  • FR
  • International Database and Gallery of Structures

Advertisement

Young M. Lee

The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Risbeck, Michael J. / Cohen, Alexander E. / Douglas, Jonathan D. / Jiang, Zhanhong / Fanone, Carlo / Bowes, Karen / Doughty, Jim / Turnbull, Martin / DiBerardinis, Louis / Lee, Young M. / Bazant, Martin Z. (2023): Data-driven control of airborne infection risk and energy use in buildings. In: Building and Environment, v. 245 (November 2023).

    https://doi.org/10.1016/j.buildenv.2023.110893

  2. Jiang, Zhanhong / Risbeck, Michael J. / Kulandai Samy, Santle Camilas / Zhang, Chenlu / Cyrus, Saman / Lee, Young M. (2023): A timeseries supervised learning framework for fault prediction in chiller systems. In: Energy and Buildings, v. 285 (April 2023).

    https://doi.org/10.1016/j.enbuild.2023.112876

  3. Risbeck, Michael J. / Bazant, Martin Z. / Jiang, Zhanhong / Lee, Young M. / Drees, Kirk H. / Douglas, Jonathan D. (2021): Modeling and multiobjective optimization of indoor airborne disease transmission risk and associated energy consumption for building HVAC systems. In: Energy and Buildings, v. 253 (December 2021).

    https://doi.org/10.1016/j.enbuild.2021.111497

  4. Risbeck, Michael J. / Bazant, Martin Z. / Jiang, Zhanhong / Lee, Young M. / Drees, Kirk H. / Douglas, Jonathan D. (2021): Quantifying the Tradeoff Between Energy Consumption and the Risk of Airborne Disease Transmission for Building HVAC Systems. In: Science and Technology for the Built Environment, v. 28, n. 2 (December 2021).

    https://doi.org/10.1080/23744731.2021.1984171

  5. Jiang, Zhanhong / Risbeck, Michael J. / Ramamurti, Vish / Murugesan, Sugumar / Amores, Jaume / Zhang, Chenlu / Lee, Young M. / Drees, Kirk H. (2021): Building HVAC control with reinforcement learning for reduction of energy cost and demand charge. In: Energy and Buildings, v. 239 (May 2021).

    https://doi.org/10.1016/j.enbuild.2021.110833

  6. Chae, Young Tae / Horesh, Raya / Hwang, Youngdeok / Lee, Young M. (2016): Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. In: Energy and Buildings, v. 111 (January 2016).

    https://doi.org/10.1016/j.enbuild.2015.11.045

Search for a publication...

Only available with
My Structurae

Full text
Structurae cooperates with
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine