Forecasting Simulations in Built Environment by Ensemble Kalman Filter (EnKF) (Data Assimilation)

Student: Danlin Hou, Ph.D. Candidate and Cheng-Chun Lin, Ph.D.

Sponsor: Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants


C. Lin and L. Wang. 2013. Forecasting simulations of indoor environment using data assimilation via an Ensemble Kalman Filter. Building and Environment. Volume 64, pp. 169-176.

C. Lin and L. Wang. 2014. Applications of Data Assimilation to Forecasting Indoor Environment. 2014 IEEE International Conference on Automation Science and Engineering (CASE). Taipei, Taiwan, August 18-22, 2014.

D. Hou, C. Lin, A. Katal and L. Wang. 2019. Forecasting Cooling Load and Energy Saving Potential by Ensemble Kalman Filter for an Institutional High-Rise Building with Hybrid Ventilation. Submitted to ISHVAC 2019. Harbin, China, 8 pages.

Research Highlights:

  • Use CFD software, FLUENT, to generate detailed data as numerical experimental data
  • Use CONTAM in the data assimilation toolbox: OpenDA
  • Forecasts are made in the time frame of days in terms of airflow infiltrations and indoor contaminant levels
  • Different data assimilation methods will be tested within OpenDA

Comparisons of deterministic sequential simulation and EnKF with the measured SF6

Predictability of the model near the second injection of SF6