A moving-window Bayesian inference procedure jointly estimates thermal parameters, airflow, occupancy trajectories, and sensor noise in a coupled CO2-temperature RC network model for buildings, achieving accurate trajectory reconstruction and low forecast errors on synthetic and physical validation.
Development of moving window state and parameter estimators under maximum likelihood and bayesian frameworks
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Data-driven moving-window Bayesian inference for transient CO2-temperature network models of buildings
A moving-window Bayesian inference procedure jointly estimates thermal parameters, airflow, occupancy trajectories, and sensor noise in a coupled CO2-temperature RC network model for buildings, achieving accurate trajectory reconstruction and low forecast errors on synthetic and physical validation.