Sensors are a crucial component in heating,ventilation,and air conditioning (HVAC) control systems,and the quality of them plays an important role in control accuracy. In the research of fault detection and control optimization,improving sensor data quality has inspiring potential in application. It has been largely limited to the application of signal processing methods in research focus,whereas a detailed analysis of the characteristics of signals from various sensors of the HVAC system has not been conducted. Therefore,this study analyzes the time-frequency domain characteristics of control sensors within HVAC systems through integrating the structural design and control logic of such systems. Additionally,the research examines the correlations between control sensors in HVAC systems. Based on statistical principles and the energy–mass dynamic laws of the equipment,this paper defines first-class (Ⅰ) correlated sensors and second-class (Ⅱ) correlated sensors. To sum up,the main contribution of this paper is conducting a fundamental study on the characteristics of control sensors within HVAC systems,providing theoretical reference for future research on HVAC system fault diagnosis and control optimization.

Turbulence induced by human movement is thought to affect the dispersion of pollutants in indoor environments. In this study, eight classical crowd scenarios were numerically simulated to investigate the effects of human movement on indoor air pollution in different scenarios. High-level simulations were performed into human movement, respiration, and heat dissipation, and differential analyses of the simulation results for different flow scenarios were conducted to investigate the interactions between individuals. Research has shown that people walking create significant wake currents within approximately 1.1 m on either side of their path and within 3–4 m behind them. When two pedestrians crossed paths, the wind speed increased significantly to 1.87 m/s compared with a single person walking at 1 m/s. The greatest mutual interference in pollutant distribution occurs when two individual cross paths are perpendicular, with a critical interference distance threshold of 2.87 m. Carbon dioxide concentrations fluctuate, surpassing 1,000 ppm within ten minutes at a density of 1.52 persons per square meter. Additionally, the dispersion of aerosol particles is significantly influenced by the relative direction of movement between individuals and pollutant sources. Calculated “safe distance” to avoid inhalation of exhaled aerosols in short flow exposure scenarios is at least 3.4 m. The personnel wake disturbance intensity was defined based on the rate of change in the velocity amplitude in the personnel wake region, the wake deformation rate, and the dissipation time. These insights can guide improvements in indoor air quality and health risk reduction in densely populated spaces.

Building air conditioning systems (ACs) can contribute to the stable operation of power grids by participating in peak load shaving programs, but the participants need a fast and accurate zone temperature prediction model, e.g., the detailed room thermal-resistance (RC) model, to improve peak shaving effect and avoid obvious thermal discomfort. However, when applying the detailed room RC model to multi-zone buildings, conventional studies mostly consider the heat transfer among neighboring rooms, which contributes little to the prediction accuracy improvement, but leads to complicated model structure and heavy computation. Thus, a distributed RC model is developed for multi-zone buildings in this study. Compared to conventional models, the proposed model considers the total heat transfer between the building and the air, and ignores the heat transfer among indoor air in neighboring rooms through internal walls with heavy thermal mass, thereby having comparable temperature prediction accuracy, simpler structure, and stronger robustness. Based on the model, the effectiveness of passive pre-cooling strategies in reducing the air conditioning loads during peak periods is investigated. Results indicate that the thermal insulation performance of opaque building envelope is quite important to the flexibility enhancement of air conditioning loads. With an uninsulated building envelope, passive pre-cooling is useless for the peak load shaving. In comparison, well insulated opaque building envelope enables the building thermal mass to be utilized through passive pre-cooling, which leads to the air conditioning loads during peak periods being further reduced by about 12%.

Current thermal sensation estimation models mostly are suitable for the sedentary condition, failing to consider the difference of human thermal sensation in different activity states. This has caused critical limitations in accurately predicting thermal sensation. Moreover, the development method of current models primarily relied on regression analysis, which ignored the non-linear characteristics between the skin temperature and thermal sensation. This paper aimed to identify the significant parameters that can accurately estimate human thermal sensation in different activity states by experimenting and developing the estimation model based on the Takagi-Sugeno (T-S) fuzzy model. A series of human subject experiments were carried out in an environment chamber. The results indicated the feasibility of using wrist skin temperature and its time differential and heart rate as variables for developing thermal sensation estimation model. After that, the T-S fuzzy model was used to develop the thermal sensation estimation models, taking into account the influence of gender. To analyze the applicability of the estimation models in an unstable condition, several experiments were further carried out in the actual built environment. The study revealed that the thermal sensation estimation model based on skin temperature and its time differential and heart rate showed a high degree of accuracy, while the estimation model based only on skin temperature and heart rate also indicated good prediction effect. In addition, the verification results illustrated that the proposed models can predict the human thermal sensation in the unstable environmental condition.