Energy and Built Environment最新文献

Occupancy detection and estimation in buildings paves the way to improve the utilization of lighting and HVAC systems, induce energy savings and enhance the well-being of the occupants. This paper presents a comparative study of state-of-art machine learning techniques that solve two different occupancy monitoring problems using environmental sensor data. One is the regression problem that estimates the actual count of occupants while the other is the classification problem which estimates the level of occupancy (empty, sparse, full). The results of the best performing machine learning techniques that solve both problems for the open dataset from the University of Southern Denmark, Odense are presented to compare the accuracy of both approaches and the ease of implementation. The impact of $C{O}_2$, temperature, and humidity features on the occupancy count/levels and detection accuracy (occupied versus unoccupied) are studied. Comprehensive analysis with different combinations of environmental features and other free features such as time-of-day along with different sampling techniques for training and testing are performed to understand how such models can be adapted for actual deployment. Our results indicate detection accuracy between 66% to 82% for different sampling schemes; with day-based sampling showing a better performance while random sampling generically showcasing lower accuracy (66.2%). The occupancy estimation (levels or counts) has accuracy in the range of 69% to 79% for random sampling and 71% to 80% for day-based sampling. Finally, results demonstrate that models based single environmental sensor data streams do not perform as well as the models with sensor fusion.

Thermal comfort of occupants is key feedback information for improving indoor environment and managing building energy use. Through analyzing inertial measurement units (IMU) data from wearable devices with machine learning, thermal comfort of occupants can be detected in a non- intrusive method. This paper proposed a dataset consisted of IMU data collected from 30 participants (14 males and 16 females, aged 23.23 ± 1.70 years, height 168.67 ± 8.02 cm, and weight 59.55 ± 10.96 kg) who wore two IMUs on their hands while performing 30 thermal comfort activities (10 cold-related, 10 hot-related, and 10 neutral activities) according to their personal habits.

The database is divided into two parts: (1) Single activities data, which includes 4500 samples acquired from experiments where each participant was asked to perform 30 thermal comfort activities individually. (2) Continuous multi-activity data, which comprise 360 samples collected while participants performed a series of randomly assigned activities in a more natural and continuous manner. The combination of these two parts provides a comprehensive dataset for both the training and testing phases of machine learning models. By offering detailed labels, this database aims to serve as a foundation for research exploring machine learning applications in detecting occupant thermal comfort, ultimately contributing to improved indoor environments and more efficient building energy management.

The high-level biosafety laboratory is not only the basic support for infectious disease prevention and control, but also interrelated with key areas such as environmental security and social security, which has attracted increasing attention. A good indoor environment is the premise to ensure the smooth progress of the experiment and biological risk prevention and control. In order to better understand the indoor environment of high-level biosafety laboratories, 19 high-level biosafety laboratories in China (with a total of 65 main rooms) were carefully selected as the test objects from December 2020 to December 2022. According to the test methods specified in the Chinese standard GB 50346, the air change, cleanliness, static pressure difference, temperature, relative humidity, and illumination were tested and analyzed. The results showed that all the measured parameters met the requirements of the Chinese standard GB 50346, and the bio-safety performance was completely satisfactory. However, individual parameters showed some overlarge values: the proportion of main rooms with cleanliness levels of 7 and 8 exceeding 50% of the lower limit for air changes was 54.5% and 69.8%, respectively; the proportion of main rooms in BSL-3-b1 laboratories with atmospheric pressure differentials exceeding 50% of the standard lower limit was 94.7%; and the atmospheric pressure differential in the main rooms of BSL-3-b2 laboratories reached a maximum of nearly -160 Pa. On the premise of ensuring the cleanliness and pressure gradient of the main room, it may be possible to reduce the air change. This study, for the first time, reveals the environmental parameters of various types and levels of biosafety laboratories, which can provide reference for the design and operation of such facilities.

Outdoor thermal comfort is crucial for creating sustainable and livable urban spaces. Studying the distribution and impact factors of the thermal environment can provide theoretical support for improving the thermal environment and spatial planning. This study investigates the influence of well-defined boundary spaces on outdoor thermal comfort and proposes design measures to improve spatial thermal comfort. High-resolution data obtained from unmanned aerial vehicles (UAVs) is integrated into outdoor microclimate simulations to enhance the accuracy and precision of the models. The Physiological Equivalent Temperature (PET) index is employed as an evaluation indicator, considering the categorization of PET values into different comfort levels. Additionally, the axial evaluation method is introduced to assess thermal comfort more accurately, reflecting the perceived thermal comfort by individuals. Through a case study in a mixed-use and clear boundary area, the research identifies the variables that influence outdoor thermal comfort and provides design guidelines to enhance spatial thermal comfort. The correlations between the spatial morphology index and PET were analyzed by multiple regression. The findings contribute to the understanding of outdoor thermal comfort in complex urban environments and offer valuable insights for the design and planning of comfortable and sustainable outdoor spaces.

Double low-E glasses are effective and well-established choices for residential buildings in temperate climatic regions of Sydney, Australia, and Tehran, Iran. The current study's measurements and field experiments have shown that using a double low-E windowpane can improve window total transmitted radiation energy (TSRE) and daylight glare factor. Nevertheless, spatial daylight autonomy (sDA) and daylight illuminance are the shortcomings of using double-low-E glasses. These implications demonstrated that using double low-E glazing is a double-edged sword. Despite its efficiency in improving energy consumption, it cannot satisfy daylight comfort requirements. Therefore, this research intends to find the most suitable solution to exploit double-low-E glasses' benefits and avoid their drawbacks. Subsequently, the genetic algorithm has been used to find the optimum window size through a multi-objective simulation by Climate Studio. The findings suggest that the optimum WWR of 10.35%–10.99% in Tehran brings the daylight comfort metrics above the threshold while the energy consumption metrics are kept at a minimum. Similarly, for Sydney, these measures are 20%–24% room length for the horizontal dimension of a window and 33%–40% room height for the vertical penetration dimension. In this way, using a double low-E window pane is justifiable for both examined regions.

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover.

The on-going COVID-19 pandemic has wrecked havoc in our society, with short and long-term consequences to people's lives and livelihoods - over 651 million COVID-19 cases have been confirmed with the number of deaths exceeding 6.66 million. As people stay indoors most of the time, how to operate the Heating, Ventilation and Air-Conditioning (HVAC) systems as well as building facilities to reduce airborne infections have become hot research topics. This paper presents a systematic review on COVID-19 related research in HVAC systems and the indoor environment. Firstly, it reviews the research on the improvement of ventilation, filtration, heating and air-conditioning systems since the onset of COVID-19. Secondly, various indoor environment improvement measures to minimize airborne spread, such as building envelope design, physical barriers and vent position arrangement, and the possible impact of COVID-19 on building energy consumption are examined. Thirdly, it provides comparisons on the building operation guidelines for preventing the spread of COVID-19 virus from different countries. Finally, recommendations for future studies are provided.

Over the course of industrial manufacturing, additional heat within the extract systems is usually released into the atmosphere and its intrinsic energy is wasted. This paper investigated a cold abatement smoke extract system for a fire testing wall furnace to determine the viability in recovering heat from the hot smoke. Three scenarios were investigated: 1) the extract system was closed and only 300°C smoke was present; 2) the system took in ambient air around the furnace and heat recovery occurred at 80°C in smoky air; 3) the smoke had been removed from the air and the temperature was 60°C. It was found that there was a significant build-up of soot on Scenarios 1 & 2 with a build-up rate of 0.25 μm/s which totalled 2.7 mm of soot after a three-hour test. The soot had a low heat transfer rate and therefore acted as an insulator on the heat exchanger which reduced the efficiency significantly of it over time. Due to this loss in efficiency, it was more viable to recover heat in Scenario 3 at 60°C in clean air than it was to recover heat at 300°C or 80°C in smoky air. The results show that having clean air was more important than a higher temperature when it came from recovering heat from a cold abatement system for a fire testing furnace. This paper contributes to reveal the possibilities of harnessing the “waste heat” for use in other applications in the vicinity of the manufacturing processes, such as heating water within a central heating plant, domestic hot water or electricity generation, or re-cycled within the industrial plant itself.

The outbreak of COVID-19 and the spread of infectious pathogens through bioaerosols have once again aroused widespread concern worldwide. Isolation ward is an important place to prevent the spread of infectious bioaerosols. However, infection of health care workers (HCWs) in the isolation ward often occurs, so it is urgent to carry out relevant research to reduce the cross-infection between HCWs and patients. In this paper, the temporal and spatial distribution characteristics of bioaerosols under three mixed ventilation modes in a single ward were studied, namely, upper supply side return air of Case 1 and side supply and side return ventilation are Case 2 and Case 3 respectively. The results show that the removal efficiency of bioaerosol in the ventilation mode of Case 3, in which directional airflow is formed from the air supply inlet to the release source and then to the exhaust outlet, is 46.6% and 67.7% higher than that of Case 1 and Case 2, respectively. In addition, ventilation methods based on mixed theory do not guarantee good air quality in the breathing zone (1.3 m to 1.7 m) of HCWs, which may increase the inhalation risk for HCWs. It is hoped that our results can provide some useful suggestions for optimizing the airflow layout of the isolation ward, reducing the risk of cross-infection, and virus elimination.

Occupants’ thermal comfort in buildings may be affected by the cool wall and warm wall, which is attributed to the effect of asymmetric radiation. However, the previous majority of the researches on asymmetric radiation were mainly about the comfort limits under thermally neutral condition within 1∼1.5 h but had not considered the effect of exposure duration and the condition beyond neutral. To investigate the human thermal comfort under an asymmetric environment caused by the cool wall and warm wall, forty-four subjects were exposed to neutral air temperature with lateral radiant asymmetries in winter and summer for 3 h. The results indicated that the cool wall caused thermal discomfort easier than the warm wall because the thermal sensation decreased and deviated from neutral with time. Subjects' sensitivity of local parts to asymmetric radiation was affected in the conditions beyond neutral, thus their acceptability to asymmetric radiation decreased. The currently used limits of radiant temperature asymmetry tended to underestimate the local discomfort due to the walls. For the conditions tested, The limits of 5 % dissatisfaction in radiant temperature asymmetry were 4.4 °C (180 min) and 1.8 °C (60 min and 120 min) for the warm wall, and 1.8 °C at 60 min for the cool wall.