Data-driven moving-window Bayesian inference for transient CO2-temperature network models of buildings
For building energy monitoring and performance assessment, this work provides a robust approach to predict temperatures under limited sensing by coupling CO2-informed airflow with thermal dynamics.
This work proposes a CO2-temperature network model for buildings that couples mass transport and thermal dynamics, and introduces a moving-window Bayesian inference procedure to calibrate the model and track time-varying conditions. The method accurately reconstructs trajectories and yields low forecast errors in both synthetic and physical experiments, with interpretable diagnostics for regime transitions.
In this work, we proposes a CO2-temperature network model that links multi-zone mass transport and thermal dynamics through shared latent drivers, airflow and occupancy. The thermal component is formulated as a resistance-capacitance (RC) network augmented with airflow-driven convective exchange, while the CO2 component is governed by inter-zonal convective transport. To calibrate the model and track time-varying operating conditions based on sparse sensing, we introduce a moving-window Bayesian inference procedure that jointly estimates thermal parameters, airflow and occupancy trajectories. The estimation also provides room-specific sensor noise levels, yielding posterior predictive forecasts with credible intervals. The framework is assessed using a controlled synthetic benchmark, and a scaled physical validation experiment using CO2 and temperature sensing. In both settings, the posterior accurately reconstructs trajectories within windows and delivers low forecast errors. When inference windows overlap abrupt regime transitions, the widened uncertainty bands and increased inferred noise levels provide an interpretable diagnostic of model-data mismatch, followed by rapid recovery once the new regime is observed. Overall, coupling CO2-informed airflow with thermal dynamics yields a robust approach for conductive and advective temperature prediction, supporting practical monitoring and energy-performance assessment under limited sensing.