•A new model is established to analyze the thermal-humidity environment in ecological community.•A method was proposed to improve the thermal comfort of the ecological community.•Distribution ...characteristics of UTCI were found in ecological community.
In this paper, the water evaporation from porous pavement is used to improve the thermal-humidity environment (THE) in pedestrian area of ecological community at the large wind and sand, and arid areas, such as Lanzhou in northwest China. A simulation model is established to analyze the THE of ecological community with the porous pavement in arid areas. And the water evaporation is considered from plant transpiration and porous pavement in this model. The simulation results are compared with the experimental results, the relative error was less than 13.5%. At the same time, the THE is also analyzed under different building layout, building spacing and building height. The results show that the average value of universal thermal climate index (UTCI) in pedestrian area increases with the building height, while the minimum value has little difference. This means that the local thermal discomfort would more likely occurs in ecological community with the building height increase. The local value of UTCI in pedestrian area decreases by about 8℃ when the building spacing is changed from 10 m to 40 m. The uniformity of UTCI is greatly improved when the building spacing rises to 25 m. The array building layout should be adopted in Lanzhou, the building spacing should be at least 25 m when the building height is 50 m. The above research results can provide theoretical guidance for improving the thermal comfort level of ecological community in Lanzhou.
This study aims to investigate students' overall and local thermal sensation & comfort in air-conditioned dormitories of university. A field survey was conducted in air-conditioned dormitories in ...Changsha, a big city in south of China with hot-humid climate in summer. This survey combined environmental parameters measurements and questionnaires. The obtained results indicated that head exerted the highest influence on overall thermal sensation, followed by calf and foot, then the influences of chest and back were comparatively lower. Such a result can be explained by physiological, environmental and local-disturbed factors. As for overall thermal comfort, it was determined by the most and the second most comfortable body parts. In this study, those two body parts usually went to head and chest. Characteristics of body parts and indoor environment explained the relationship between overall and local thermal comfort. Moreover, in most cases, overall comfort was higher than local comfort of any other body part due to the high-level control over the indoor environment. Nevertheless, because of various factors in real environment, there were significant differences of local thermal sensation among current studies. Therefore, more studies are still in need to establish a more universal model.
•Head, calf, foot, chest and back influenced overall thermal sensation.•Local thermal sensation and its effect were decided by various factors.•Overall thermal comfort was determined by the two most comfortable body parts.•Overall comfort was higher than local due to effective control.
•Characteristics of the non-intrusive thermal comfort detection are defined.•A camera network is introduced to assess thermal comfort in multi-occupancy spaces.•Thermal and RGB-D cameras are fused to ...measure facial skin temperature.•Subjects can have flexible postures and movements during the data collection.•Facial mean skin temperature can serve as an indicator of one’s thermal comfort.
About 40% of the energy produced globally is consumed within buildings, primarily for providing occupants with comfortable work and living spaces. However, despite the significant impacts of such energy consumption on the environment, the lack of thermal comfort among occupants is a common problem that can lead to health complications and reduced productivity. To address this problem, it is particularly important to understand occupants’ thermal comfort in real-time to dynamically control the environment. This study investigates an infrared thermal camera network to extract skin temperature features and predict occupants’ thermal preferences at flexible distances and angles. This study distinguishes from existing methods in two ways: (1) the proposed method is a non-intrusive data collection approach which does not require human participation or personal devices; (2) it uses low-cost thermal cameras and RGB-D sensors which can be rapidly reconfigured to adapt to various settings and has little or no hardware infrastructure dependency. The proposed camera network is verified using the facial skin temperature collected from 16 subjects in a multi-occupancy experiment. The results show that all 16 subjects observed a statistically higher skin temperature as the room temperature increases. The variations in skin temperature also correspond to the distinct comfort states reported by the subjects. The post-experiment evaluation suggests that the networked thermal cameras have a minimal interruption of building occupants. The proposed approach demonstrates the potential to transition the human physiological data collection from an intrusive and wearable device-based approach to a truly non-intrusive and scalable approach.
In this study, various energy conservation measures (ECMs) on heating, ventilating and air conditioning (HVAC) and lighting systems for a four-storied institutional building in sub-tropical (hot and ...humid climate) Queensland, Australia are evaluated using the simulation software called DesignBuilder (DB). Base case scenario of energy consumption profiles of existing systems are analysed and simulated first then, the simulated results are verified by on-site measured data. Three categories of ECMs, namely major investment ECMs (variable air volume (VAV) systems against constant air volume (CAV); and low coefficient of performance (COP) chillers against high COP chillers); minor investment ECMs (photo electric dimming control system against general lighting, and double glazed low emittance windows against single-glazed windows) and zero investment ECMs (reset heating and cooling set point temperatures) are evaluated. It is found that the building considered in this study can save up to 41.87% energy without compromising occupancies thermal comfort by implementing the above mentioned ECMs into the existing system.
Els treballadors d'oficines es troben la major part del temps a l'interior d'un edifici, i amb això, les variables fisicoambientals comencen a presentar una importància en la productivitat i ...l'acompliment. Aquest estudi relaciona els models de machine learning amb el comportament dels ocupants i la productivitat autoavaluada que presenten, mitjançant l’ús de diferents models. Aquests models es van implementar per reconèixer i comparar quins permeten estimar de millor manera aquest comportament, en particular, la productivitat autoavaluada que les persones senten al seu espai de treball. Per això, es van recollir les variables fisicoambientals i la percepció dels ocupants de diversos edificis d'oficina a la ciutat de Concepción. Aquest estudi aconsegueix comparar l'exercici de quatre models de machine learning (arbre de decisions, K-Nearest Neighbor, model de bais i xarxa neuronal), l'exercici d'aquests es va mesurar mitjançant els indicadors anomenats Accuracy, Precision i Recall. Aquests models es van implementar tant per a una base de dades original com en una base de dades balancejada, per després comparar els resultats obtinguts. Es pot establir que hi ha una relació entre les variables fisicoambientals i la productivitat autoavaluada dels treballadors. Així mateix, es pot esmentar que la xarxa neuronal és el model que millor descriu aquesta relació i, per tant, el que aconsegueix millor. Aquest estudi permet un apropament a comprendre el comportament dels ocupants des d'una perspectiva del machine learning.
Office workers spend most of their time inside a building, and as a result, physical-environmental variables begin to play a crucial role in their productivity and performance. This study establishes a connection between machine learning models and the behavior of occupants and the self-assessed productivity they exhibit, through the use of various models. These models were implemented to identify and compare which of them better estimate this behavior, particularly the self-assessed productivity that individuals experience in their workplace. To accomplish this, physical-environmental variables, and the perceptions of occupants from various office buildings in the city of Concepción were collected. This study successfully compares the performance of four machine learning models (decision tree, K-Nearest Neighbor, Bayesian model, and neural network). Their performance was measured using indicators known as Accuracy, Precision, and Recall. These models were applied to both an original database and a balanced database, followed by a comparison of the results obtained. It can be established that there is a relationship between physical-environmental variables and the self-assessed productivity of workers. Furthermore, it can be mentioned that the neural network is the model that best describes this relationship and, therefore, achieves the highest performance. This study provides an approach to understanding occupant behavior from a machine learning perspective
Los trabajadores de oficinas se encuentran la mayor parte del tiempo al interior de un edificio, y con ello, las variables físico-ambientales comienzan a presentar una importancia en la productividad y desempeño de ellos. Este estudio relaciona los modelos de machine learning con el comportamiento de los ocupantes y la productividad autoevaluada que presentan, mediante el uso de diferentes modelos. Estos modelos se implementaron para reconocer y comparar cuáles de ellos permiten estimar de mejor forma este comportamiento, en particular, la productividad autoevaluada que las personas sienten en su espacio de trabajo. Para ello, se recogieron las variables físico-ambientales y la percepción de los ocupantes de diversos edificios de oficina en la ciudad de Concepción. Este estudio logra comparar el desempeño de cuatro modelos de machine learning (árbol de decisiones, K-Nearest Neighbor, modelo de bayes y red neuronal), el desempeño de estos se midió mediante los indicadores denominados Accuracy, Precision y Recall. Estos modelos se implementaron tanto para una base de datos original como en una base de datos balanceada, para luego comparar los resultados obtenidos. Se puede establecer que existe una relación entre las variables físico-ambientales y la productividad autoevaluada de los trabajadores. Así mismo, se puede mencionar que la red neuronal es el modelo que mejor describe esta relación y, por ende, el que mejor desempeño logra. Este estudio permite un acercamiento a comprender el comportamiento de los ocupantes desde una perspectiva del machine learning.
In order to improve the thermal performance of Trombe wall and make its active in a whole year, this paper proposed a novel Trombe wall integrated with double layers phase change material (PCM) named ...PCM Trombe wall. This paper studied the thermal performance of PCM Trombe wall numerically in winter and summer in Wuhan. The results showed that the PCM Trombe wall can improve indoor overheating in summer, and reduce indoor temperature fluctuations in winter. Therefore, the PCM Trombe wall can improve indoor thermal comfort and reduce cooling/heating load in the whole year compared with the traditional Trombe wall.
Radiative cooling in textiles is one of the important factors enabling cooling of the human body for thermal comfort. In particular, under an intense sunlight environment such as that experienced ...with outdoor exercise and sports activities, high near-infrared (NIR) reflectance to block sunlight energy influx along with high IR transmittance in textiles for substantial thermal emission from the human body would be highly desirable. This investigation demonstrates that a nanoscale geometric control of textile structure alone, instead of complicated introduction of specialty polymer materials and composites, can enable such desirable NIR and IR optical properties in textiles. A diameter-dependent Mie scattering event in fibers and associated optical and thermal behavior were simulated in relation to a nonwoven, nanomesh textile. As an example, a nanomesh structure made of PVDF (polyvinylidene fluoride) electrospun fibers with ∼600 nm average diameter was examined, which exhibited a significant radiative cooling performance with over 90% solar and NIR reflectance to profoundly block the sunlight energy influx as well as ∼50% IR transmittance for human body radiative heat dissipation. An extraordinary cooling effect, as much as 12 °C, was obtained on a simulated skin compared to the normal textile fabric materials. Such a powerful radiative cooling performance together with IR transmitting capability by the nanomesh textile offers a way to efficiently manage sunlight radiation energy to make persons, devices, and transport vehicles cooler and help to save energy in an outdoor sunlight environment as well as indoor conditions.
Understanding the drivers leading to individual differences in human thermal perception has become increasingly important, amongst other things due to challenges such as climate change and an ageing ...society. This review summarizes existing knowledge related to physiological, psychological, and context-related drivers of diversity in thermal perception. Furthermore, the current state of knowledge is discussed in terms of its applicability in thermal comfort models, by combining modelling approaches of the thermoneutral zone (TNZ) and adaptive thermal heat balance model (ATHB). In conclusion, the results of this review show the clear contribution of some physiological and psychological factors, such as body composition, metabolic rate, adaptation to certain thermal environments and perceived control, to differences in thermal perception. However, the role of other potential diversity-causing parameters, such as age and sex, remain uncertain. Further research is suggested, especially regarding the interaction of different diversity-driving factors with each other, both physiological and psychological, to help establishing a holistic picture.
Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun ...irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. This article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.
•A thorough analysis of HVAC system control approaches is provided.•A comprehensive comparative analysis was undertaken to assess the MPC’s performance.•A comprehensive analysis of MPC variations and their respective applications is offered.•An entire section is devoted to the current difficulties and potential for MPC HVAC control.
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