Breathing zone airflow4/17/2023 The model performs well on accuracy, simplicity and robustness. Lastly, the first 20% of the approximation component is used to model the entire period, reducing measurement costs. The decomposition level is the key hyperparameter to denoise the air temperature signal for approaching the modelled value. Thirdly, the dynamic models for the approximation component are consistent with that for the raw air temperature signal, with an improved coefficient of determination of 0.91–0.97, and the RMSE of 0.02–0.05 ☌. Secondly, the rate of air temperature variation positively correlates to the air velocity variation between the idle/duty period and the steady state, as well as the supply air velocity deviation, with the Pearson correlation coefficient value of about 0.7 (p < 0.01). Firstly, the dynamic model can accurately predict the raw air temperature, with the root mean square errors (RMSE) of 0.04–0.07 ☌. The raw air temperature signal is divided into an approximation component maintaining the major fluctuations and a detail component representing the stochastic volatility, by a validated denoising structure using wavelet decomposition. Exception: Breathing zone outdoor airflow shall be permitted tobe reduced zero for zones in occu-pied standby mode for the occupancy categories indicated in Table 6.2.2.1, provided that. The model is validated through experiments of four cases and twelve measurement points at the height of 1.1 m each case. 6.2.7.1.2 For DCV zones in the occupied mode, breath-ing zone outdoor airflow (V bz) shall be not less than the build-ing component (R a × A z) for the zone. This study proposes a dynamic physical-empirical hybrid model for the transient air temperature in a stratum-ventilated classroom, concerning specific locations and dynamics in the breathing zone, accuracy to the second. Transient thermal conditions in the breathing zone created by pulsating air supply, can improve thermal comfort compared with steady air supply under stratum ventilation.
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