Springer Science and Business Media LLC

As an important factor in the investigation of building energy consumption, occupant behavior (OB) has been widely studied on the building level. However so far, studies of OB modelling on the district scale remain limited. Indeed, district-scale OB modelling has been facing the challenges from the scarcity of district-scale data, modelling methods, as well as simulation application. This study initiates the extrapolation of occupancy modelling methodology from building level to district scale through proposing modelling methods of inter-building movements. The proposed modelling methods utilize multiple distribution fittings and Bayesian network to upscale the event description methods from inter-zone movement events at the building level to inter-building movement events at the district level. This study provides a framework on the application of the proposed modelling methods for a university campus in the suburbs of Shanghai, taking advantages of data sensing, monitoring and survey techniques. With the collected campus-scale occupancy data, this paper defines five patterns of inter-building movement. One pattern represents the dominated inter-building movement events for one kind of students in their daily campus life. Based on the quantitative descriptions for various inter-building movement events, this study performs the stochastic simulation for the campus district, using Markov chain models. The simulation results are then validated with the campus-scale occupancy measurement data. Furthermore, the impact of inter-building movement modelling methods on building energy demand is evaluated for the library building, taking the deterministic occupancy schedules suggested by current building design standard as a baseline.

Determining required sample size is one of the critical pathways to reproducible, reliable and robust results in human-related studies. This paper aims to answer a fundamental but often overlooked question: what sample size is required in surveys of occupant responses to indoor environmental quality (IEQ). The statistical models are introduced in order to promote determining required sample size for various types of data analysis methods commonly used in IEQ field studies. The Monte Carlo simulations are performed to verify the statistical methods and to illustrate the impact of sample size on the study accuracy and reliability. Several examples are presented to illustrate how to determine the value of the parameters in the statistical models based on previous similar research or existing databases. The required sample size including “worst” and “optimal” cases in each condition is obtained by this method and references. It is indicated that 385 is a “worst case” sample size to be adequate for a subgroup analysis, while if the researcher has an estimate of the study design and outcome, the “optimal case” sample size can potentially be reduced. When the required sample size is not achievable, the uncertainty in the result can properly interpret via a confidence interval. It is hoped that this paper would fill in the gap between statistical analysis of sample size and IEQ field research, and it can provide a useful reference for researchers when planning their own studies.

Research on the influence of thermal radiation of tree canopies to adjacent exterior walls has relevance to the selection of tree species and the spatial arrangement of trees for urban planning. In the last decade, there have been many studies on the influence of tree shadows on the thermal environment and energy consumption of buildings. However, there is a lack of research on how trees affect the thermal radiation of adjacent buildings, when they do not cast direct shadows on the walls. In view of this, a combination of experiment and simulation was used to explore the influence of spherical canopy on the intensity changes of net long-wave thermal radiation (TRDL) and net short-wave thermal radiation (TRDS) absorbed by the adjacent wall. Both measured and simulated results show that the tree canopy has a TRD (the sum of TRDL and TRDS) effect on the south wall of adjacent buildings in summer. The peak of TRD from the tree to the adjacent wall was obtained by ENVI-met under 27 scenarios. A functional relationship was further given between the peak TRD and the canopy diameter (DC), the minimum distance between wall and tree canopy (DW-T). Moreover, the influence of DC, DW-T and leaf area density (LAD) on TRD was discussed by simulation. Additionally, the TRD of canopy decays exponentially in the horizontal direction and linearly in the vertical direction of the wall. The above methods and results can guide the selection of tree species, green space design around buildings and the evaluation of the influence of trees on indoor cooling energy consumption in summer.

Các chiến lược vận hành có ảnh hưởng đến hiệu quả năng lượng của tòa nhà. Để cải thiện hiệu quả năng lượng của tòa nhà, cần áp dụng các chiến lược vận hành phù hợp cho thiết bị trong tòa nhà. Do đó, việc xác định các chiến lược vận hành hiện có là cần thiết cho việc nâng cao các chiến lược vận hành. Một khung dựa trên khai thác dữ liệu (DM) được đề xuất trong bài báo này nhằm tự động xác định các chiến lược vận hành của tòa nhà. Khung này bao gồm cây phân loại và hồi quy (CART) và phương pháp khai thác quy tắc liên kết có trọng số (WARM), hướng đến ba loại chiến lược điều khiển dựa trên quy tắc: điều khiển bật/tắt, điều khiển theo trình tự (cho thiết bị cùng loại), và điều khiển phối hợp (cho thiết bị khác loại). Hiệu suất của khung này được xác thực với dữ liệu từ hệ thống đo điện năng và kết quả xác định bằng tay dựa trên khảo sát tại chỗ của ba tòa nhà ở Thượng Hải. Kết quả xác thực cho thấy rằng khung đề xuất có khả năng xác định chính xác và tự động các chiến lược vận hành của tòa nhà. Được triển khai trên phần mềm gốc có tên BOSA (Phân tích Chiến lược Vận hành Tòa nhà), khung này hứa hẹn sẽ được sử dụng trong lĩnh vực kỹ thuật để nâng cao hiệu quả công việc xác định chiến lược vận hành tòa nhà.

The global concern over indoor air pollution in public vehicles has grown significantly. With a focus on enhancing passengers’ comfort and health, this study endeavors to investigate the distribution characteristics of formaldehyde within a high-speed train cabin by employing a computational fluid dynamics (CFD) model which is experimentally validated in a real cabin scenario. The research focuses on analyzing the impact of air supply modes, temperature, relative humidity, and fresh air change rate on the distribution and concentration of formaldehyde. The results demonstrate that the difference in average formaldehyde concentration between the two air supply modes is below 1.3%, but the top air supply mode leads to a higher accumulation of formaldehyde near the sidewalls, while the bottom air supply mode promotes a more uniform distribution of formaldehyde. Furthermore, the temperature, relative humidity, and fresh air change rate are the primary factors affecting formaldehyde concentration levels, but they have modest effects on formaldehyde’s distribution pattern within the cabin. As the temperature and relative humidity increase, the changes in formaldehyde concentrations in response to variations in these factors become more evident. Importantly, the formaldehyde concentration may surpass the standard limit of 0.10 mg/m3 if the fresh air change rate falls below 212 m3/h. This research provides a systematic approach and referenceable results for exploring formaldehyde pollution in high-speed train cabins.

Changing climate intensifies heat stress, resulting in a greater risk of workplace productivity decline in timber office buildings with low internal thermal mass. The impact of climate change induced heat exposure on indoor workplace productivity in timber office buildings has not been extensively researched. Therefore, further investigation to reduce the work capacity decline towards the end of the century is needed. Here, heat exposure in a net zero-carbon timber building near Brussels, Belgium, was evaluated using a reproducible comparative approach with different internal thermal mass levels. The analysis indicated that strategies with increased thermal mass were more effective in limiting the effects of heat exposure on workplace productivity. The medium and high thermal mass strategies reduced workplace productivity loss to 0.1% in the current, 0.3% and 0.2% in the midfuture, and 4.9% and 3.9% for future scenarios. In comparison, baseline with low thermal mass yielded a decline of 2.3%, 3.3%, and 8.2%. The variation in maximum and minimum wet-bulb globe temperatures were also lower for medium and high thermal mass strategies than for low thermal mass baseline. The study findings lead to the formulation of design guidelines, identification of research gaps, and recommendations for future work.