A novel occupant-centric stratum ventilation system using computer vision: Occupant detection, thermal comfort, air quality, and energy savings

Parkinson T., Schiavon S., Kim J., Betti G.

Previously unpublished data from over 600 office buildings in the Center for the Built Environment (CBE) Occupant Survey database are used to perform a systematic analysis of dissatisfaction in contemporary workspaces. A total of 81% of respondents expressed dissatisfaction with at least one aspect of their workspace, and 67% with more than one. Acoustics were the most common source of dissatisfaction, particularly related to people talking, speech privacy, and phones. Other challenges included a perceived lack of control over the temperature and insufficient space, along with other associated problems of densely populated offices. The analysis shows that context matters when understanding occupant dissatisfaction. Occupants of open-plan offices with low or no partitions were almost twice as likely to complain about their workspace than someone in a private, enclosed office. Being near a window decreased the likelihood of dissatisfaction compared with those who were not near a window. There was a clear relationship between self-perceived performance and satisfaction with the indoor environment. Dissatisfaction profiles found that acoustics, space, and privacy-related items co-occur for many occupants dissatisfied with more than one workspace aspect. Practical relevance Post-occupancy surveys are a useful tool for evaluating whether an office environment supports occupants while conducting their work. While highlighting the successes is important, complaints from dissatisfied occupants can identify issues and pinpoint reasons why spaces do not meet expectations. The reported challenges generally relate to the simultaneous reduction in control and personalization with increasingly open and densely populated layouts. Occupant dissatisfaction may impact performance given the reported relationship between satisfaction with the environment and feeling supported by the workspace to complete work tasks. The themes emerging from this analysis identify common dissatisfaction sources that can serve as an empirical basis to identify common problems in contemporary workspace designs.

Haidar N., Tamani N., Ghamri-Doudane Y., Boujou A.

Optimizing Building energy consumption is a key solution to reducing their environmental impact. In this context, Information Technology can be harnessed by deploying sensors inside buildings, to collect relevant data about both energy consumed and occupant behavior, since occupants influence building appliances, such as HVAC, lights, and hot water tanks. Predicting room occupancy can be a solution to heat/cool rooms for instance. But, as prediction models are not often accurate, we may face situations where HVAC is activated while the rooms are empty or vice-versa, leading to either a waste of energy or a lack of occupants’ comfort. To predict user behavior, detect prediction errors, and correct the model, we introduce a graph mining-based optimization method that combines an occupant behavior prediction model and a selective reinforcement learning method, where error detection relies on sensors that detect real-time occupancy of rooms. We experimented with our approach on simulated data and results showed that, compared to conventional HVAC management, our model can reduce up to 57.8% of HVAC energy consumption, and provide up to 94.3% of occupants’ comfort when using the prediction method only, and up to 80.1% of HVAC energy consumption, and provide up to 97% of occupants’ comfort when using the reinforcement method to correct prediction errors. • The main objective is to ensure building occupants’ thermal comfort while optimizing building energy consumption. • A combined AI-based approaches is proposed: Graph Mining and Reinforcement Learning to optimize building energy usage while ensuring occupant comfort. • Occupancy prediction is used to schedule the functioning of the HVAC inside the rooms. • Room occupancy status is tracked based on a real-time data collected by sensors deployed inside a building. • Occupancy tracking is used to correct the prediction errors by deploying a reward reinforcement strategy. • Selective reinforcement method is also utilized to penalize the rooms that are occupied during a short period of time. • Experiments carried out on real-world building occupancy datasets. • Experiments results showed that our approach allows saving up to 80.1% of HVAC energy consumption and ensuring up to 97% of thermal comfort for building occupants.

Choi H., Lee J., Yi Y., Na H., Kang K., Kim T.

Recently, many researchers have become interested in occupant information and occupant-centric control (OCC) strategies, aiming for efficient building operation. Combining a camera with deep learning (hereafter referred to as deep vision-based occupancy counting) is a very effective method for occupancy counting, but there are not many studies evaluating its experimental performance. Furthermore, there are insufficient studies on implementing control using deep vision. The purpose of this study was to experimentally evaluate the performance of deep vision-based occupancy counting and to implement deep vision-based OCC in reality. First, we evaluated the performance of deep vision-based occupancy counting for six offices. Second, we implemented a deep vision-based energy recovery ventilator control strategy in a small office and compared the indoor air quality and energy consumption with those from traditional control strategies. As a result, deep vision-based occupancy counting showed significantly higher performance (root mean square error (RMSE): 0.883, normalized RMSE (NRMSE): 0.141). The larger the floor area, the more frequently the prediction of the number of occupants was lower than the actual number. The control results showed that deep vision-based ventilation control could properly maintain the indoor CO 2 concentration with 24–35% lower ventilation rates compared to traditional ventilation control strategies. Furthermore, the proposed strategy was effective in reducing the electrical energy consumption of energy recovery ventilator and heat pump. • Deep vision-based occupancy counting showed high performance in six offices. • Deep vision-based ventilation control was implemented successfully. • Presented ventilation control maintained acceptable air quality with low ventilation rates. • Presented ventilation control was effective in reducing energy consumption of ventilator and heat pump.

Choi E.J., Park B.R., Kim N.H., Moon J.W.

The aim of this study is to estimate real-time clothing insulation (R-CLO) and to evaluate the effectiveness of predicted mean vote (PMV)-based control on thermal comfort and electrical energy. For this purpose, an image-processing R-CLO model was developed to estimate the clothing insulation for various ensembles of garments worn by the occupants. The R-CLO model classified 16 individual garments and estimated the total clothing insulation for various ensembles based on these garments. Performance testing using the PMV output from the R-CLO model was conducted. The resulting PMV-based control changed the indoor set temperature according to changes in the clothing insulation, which improved the thermal comfort of the occupants when compared with existing control methods. Even though the proposed control method established a comfortable indoor environment for all clothing conditions, but also affected the electrical energy. The electrical energy is increased as the clothing insulation increased. This study confirmed the potential of comfort-driven control using a vision-based R-CLO model and verified that actual clothing information is required to achieve thermal comfort in the real building as well as to operate the system considering energy. • A vision-based model for estimating the real-time clothing insulation (R-CLO) was developed. • The model estimates the various ensemble insulation based on the classified garment. • The effect of the R-CLO on thermal comfort and energy use was tested experimentally. • The proposed control method has advantageous in thermal comfort than conventional methods. • The electrical energy of the air conditioning system is changed by the type of clothing ensemble.

Liu Y., Liu Y., Shao X., Liu Y., Huang C., Jian Y.

Different thermal preferences from occupants in a shared space must be satisfied simultaneously. For a ventilated room with multiple air supply inlets, differentiated thermal environment parameters can be maintained in different subzones by liberating the unified air supply parameter adjustment from all the air supply inlets and subsequently adopting independent adjustment of different groups of air supply inlets near different subzones. In this study, the achievable difference level of thermal environment was investigated numerically based on the stratum ventilation. Primarily, the effect of independently regulating the air supply directions and velocities from two inlet groups on the difference in the local thermal environment between subzones was analyzed. The position of occupants and heat distribution were considered as the influencing factors. Further, the predicted mean vote (PMV) index was utilized to evaluate the cool or warm degree of the local thermal environment while the draught rate (DR) and percentage dissatisfied (PD) indices were utilized to evaluate the local thermal discomfort. The results obtained indicated that regulating air supply direction significantly affected the differentiated thermal environment, with the achievable ΔPMV between subzones up to 1.46. Moreover, the increase in air supply velocity based on the appropriate air supply direction further expanded the ΔPMV. In addition, the change in heat source position resulted in a ΔPMV decrease of 0.36. No strong draught risk and local thermal discomfort caused by the vertical temperature difference were observed. This study is expected to provide guidance for the maintenance of demand-oriented zoning thermal environment. • Differentiated thermal environments in multi-occupied subzones were investigated. • Differential potential was revealed by adjusting air supply directions and velocities. • Regulating air supply direction maintains ΔPMV between occupied subzones up to 1.46 • Regulating air supply velocity based on appropriate air supply direction expands ΔPMV. • No draught risk and local thermal discomfort are found during zonal ventilation.

Kong M., Dong B., Zhang R., O'Neill Z.

• Seamless integration of three occupant sensing technologies into a real-time building heating, ventilation and air conditioning control system. • Side-by-side comparison and experiment set up to understand the performance of the occupancy-based control. • Subjective evaluation regarding both thermal comfort and perceived air quality. • Analysis about sensor accuracy and its effectiveness on energy-saving and thermal comfort. Building sensing technologies have evolved rapidly in the last two decades in aid of monitoring building environment and energy system performance. A series of occupancy sensing systems were developed to track the occupant behavior in the indoor space. Occupancy-based building system control is defined as a control method that adjusts the building system operation schedules and setpoints based on the measured occupant behavior and has been identified as a smart building control strategy that can improve building energy efficiency as well as occupant comfort. Some studies demonstrated energy-saving potential and comfort-maintaining capability from occupancy-based control. This study adopted a first-of-its-kind side-by-side experimental approach to quantify the performance of the occupancy-based control in commercial buildings. Three state-of-the-art occupancy sensing technologies were integrated into the real-time Heating, Ventilation, and Air-Conditioning (HVAC) system control in this study. Their detection accuracy and its effectiveness on energy-saving and thermal comfort were analyzed. It was found that the occupancy-based control can maintain good thermal comfort and perceived indoor air quality with a satisfaction ratio greater than 80%. Although the daily energy-saving varied with occupancy sensor accuracy and outdoor environment conditions, the weekly averaged energy saving was between 17 and 24%.

Choi H., Um C.Y., Kang K., Kim H., Kim T.

• Field test results of occupancy-counting method using the latest computer vision technology. • High counting performance (NRMSE: 0.0435) in a small office but lower (NRMSE: 0.0918) in a larger office. • A survey was conducted on whether vision-based occupancy counting violates privacy. • Simulations showed that occupancy-centric control strategies in a small office could reduce annual energy consumption by up to 10.2%. Recently, several researchers have attempted to detect occupancy information and use it to reduce building energy. Although occupancy counting using a camera and deep learning is a very effective method that has recently emerged, there are few cases in which the experimental performance and utilization have been comprehensively evaluated. The purpose of this study is to comprehensively evaluate the applicability of vision-based occupancy counting using the latest deep learning model. First, this study experimentally evaluated the performance of the vision-based occupancy-counting method in two offices and investigated the user's acceptance of this method using a questionnaire. Second, the energy-saving performance of various occupancy-centric control strategies applied to HVAC and lighting was analyzed. Experimental results showed high performance for a small office of fewer than five people (NRMSE: 0.0435) and lower performance in a larger office (NRMSE: 0.0918). In addition, despite several occupants feeling privacy invasion by the camera, they responded that the system could be accommodated to reduce building energy. Simulation results indicated that occupancy-centric control using the number of occupants could reduce annual HVAC and lighting energy in small offices by 10.2%. In addition, among several occupancy-centric control strategies, modulating the outdoor airflow rate was found to be the most effective in saving energy.

Virasova A.Y., Klimov D.I., Khromov O.E., Gubaidullin I.R., Oreshko V.V.

Formulation of the problem. Over the past few years, there has been little progress in object detection techniques. The most efficient are complex computational ensemble methods, which usually combine several low-level image properties with high-level properties. However, every day interest in artificial intelligence is growing, and the idea of using neural networks on board a spacecraft, with the possibility of making decisions and issuing one-time commands, is very promising, since it makes it possible to analyze a large data stream in real time without resorting to ground station, thereby not losing information when transmitting a packet. The purpose of the work is to conduct research on the possibility of effective use of modern models of neural networks, to develop a methodology for their use in the problem of object detection and analysis of the element base for hardware implementation with the possibility of using convolutional neural networks for thermovideotelemetry on board a spacecraft. Results of work. An approach has been formulated that combines two key ideas: 1) application of high-throughput convolutional neural networks for downward processing of image regions in order to localize and segment objects; 2) preliminary training for the auxiliary task, followed by fine tuning of the domain, which gives a significant increase in performance in the case when the training data is insufficient. The analysis of the element base for the possibility of hardware implementation of neural networks on board a spacecraft using electrical radio products of domestic and foreign production is carried out. Practical significance. The efficiency of preliminary network training for an auxiliary task is shown, followed by fine tuning of the subject area. A technique is described that makes it possible to increase the average accuracy of detecting objects in an image by more than 30%. The analysis of the existing element base, the possibility of hardware implementation of neural networks on board the spacecraft using electrical radio products of domestic and foreign production, as well as the criteria for selecting key elements.

Choi H., Na H., Kim T., Kim T.

Efforts have been made to estimate clothing insulation in real time, an element of thermal comfort for occupants. Nevertheless, an effective method to estimate clothing insulation in real time is lacking. In addition, there has been little debate on how to apply clothing insulation to building control in practice. The purpose of this study is to propose a method for estimating clothing insulation using deep learning-based vision recognition, which has recently attracted attention and implement building control based on clothing insulation. The study also evaluates the significance of the method in effective building control. The results demonstrated that the proposed framework, CloNet, showed an accuracy of 94% for the validation image dataset and 86% for the actual built environment. In addition, we proved that the proposed vision-based estimation method is very fast and practical for estimating clothing insulation. The control experiment showed that the CloNet-based predicted mean vote (PMV) control changed the set temperature in response to changes in the subject's clothing. Compared to the traditional PMV control, the CloNet-based PMV control improved the thermal preference and thermal comfort vote. These results prove that clothing insulation estimation can be useful for building control. • A real-time vision-based clothing insulation estimation method, CloNet, is proposed. • In a built environment, CloNet's average estimation accuracy was 86%. • PMV control using CloNet and IoT devices was implemented. • CloNet-based PMV control was effective at improving thermal comfort.

Tien P.W., Wei S., Calautit J.K., Darkwa J., Wood C.

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.

Wang J., Huang J., Feng Z., Cao S., Haghighat F.

Ventilation plays an important role in prevention and control of COVID-19 in enclosed indoor environment and specially in high-occupant-density indoor environments (e.g., underground space buildings, conference room, etc.). Thus, higher ventilation rates are recommended to minimize the infection transmission probability, but this may result in higher energy consumption and cost. This paper proposes a smart low-cost ventilation control strategy based on occupant-density-detection algorithm with consideration of both infection prevention and energy efficiency. The ventilation rate can be automatically adjusted between the demand-controlled mode and anti-infection mode with a self-developed low-cost hardware prototype. YOLO (You Only Look Once) algorithm was applied for occupancy detection based on video frames from surveillance cameras. Case studies show that, compared with a traditional ventilation mode (with 15% fixed fresh air ratio), the proposed ventilation control strategy can achieve 11.7% energy saving while lowering the infection probability to 2%. The developed ventilation control strategy provides a feasible and promising solution to prevent transmission of infection diseases (e.g., COVID-19) in public and private buildings, and also help to achieve a healthy yet sustainable indoor environment.

Rahman H., Han H.

Demand-controlled ventilation (DCV) is commonly implemented to provide variable amounts of outdoor air according to an internal ventilation demand. The objective of the present study is to investigate the applicability and the performance of occupancy-based DCV schemes in comparison with time-based and CO2-based DCV schemes. To do this, we apply the occupancy estimation method by the Bayes theorem to control the ventilation rate of an office building in real-time. We investigated six cases in total (two cases for each control scheme). Experiments were conducted in a small office room with controllable ventilation equipment and relevant sensors. The observed results indicated that the occupancy-based schemes relying on Bayes theorem could be applied successfully to perform continuous control of ventilation rates without causing recursive problems. Additionally, we discussed the time delays associated with the control procedure, including dispersion time, sensor-response time, and data processing time. Finally, we compared the performance of the proposed approach in six DCV cases in terms of a resultant indoor CO2 level and the total ventilation-air volume. We concluded that DCV control based on both occupancy and floor area provided the best conformity to the ASHRAE standard among the analyzed schemes.

Pang Z., Chen Y., Zhang J., O'Neill Z., Cheng H., Dong B.

The occupant-centric control (OCC) is receiving an increasing attention since it could reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CPBM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis, which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones, was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in the majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.

Tien P.W., Wei S., Calautit J.K., Darkwa J., Wood C.

This paper introduces a vision-based deep learning approach that enables the detection and recognition of occupants’ activities within building spaces. The data can feed into building energy management systems through the establishment of occupancy heat emission profiles, which can help minimise unnecessary heating, ventilation, and air-conditioning (HVAC) energy loads and effectively manage indoor conditions. The proposed demand-driven method can enable HVAC systems to adapt and make a timely response to dynamic changes of occupancy, instead of using “static” or fixed occupancy operation schedules, historical load, and time factor. Based on a convolutional neural network, the model was developed to enable occupancy activity detection using a camera. Training data was obtained from online image sources and captured images of various occupant activities in office spaces. Tests were performed by real-time live detection and predictions of occupancy activities in buildings. Initial activities response includes sitting, standing, walking, and napping. Average detection accuracy of 80.62% was achieved. The detection formed the real-time occupancy heat emission profiles known as the Deep Learning Influenced Profile. Along with typical ‘scheduled’ office occupancy profiles, a building energy simulation (BES) tool was used to further assess the framework. An office space in Nottingham, UK was selected to test the proposed method and modelled using building simulation. Using the deep learning detection method, the results showed that the occupancy heat gains could be represented more accurately in comparison to using static office occupancy profiles. The accurate detection of occupants and their activities can also be used to effectively estimate CO2 concentration. The information can be useful for modulating ventilation systems leading to better indoor environmental quality. Overall, this initial approach of the study showed the capabilities of this framework for detecting occupancy activities and providing reliable predictions of building internal gains.

Xie J., Li H., Li C., Zhang J., Luo M.

• A literature review to occupant-centric thermal comfort studies was performed. • Many variables and data-collecting sensors were utilized to support the approach. • Data-driven thermal comfort models got a median predicting accuracy of 84% • Occupant-centric thermal comfort control could save 22% energy and improve 29.1% thermal comfort. • Challenges and opportunities in the field were discussed. Ensuring occupants’ thermal comfort and work performance is one of the primary objectives for building environment conditioning systems. In recent years, there emerged many occupant-orientated technologies aiming to optimize thermal comfort while saving energy. These attempts offered opportunities to move the indoor thermal environment control from the one-fits-all approach toward a new paradigm with occupant-centric merits. A timely review of this emerging field would help to fill the knowledge gap and provide new insights for future research and practice. This study performed a literature review to summarize recent occupant-centric thermal comfort practices following a framework with three themes: sensing, predicting, and controlling. The results show that occupant-centric thermal comfort control has become a hot research topic in recent years. A wide range of variables and data-collecting sensors were utilized to support the concept. Among all the potential variables, occupants’ comfort feedback, skin temperature, and air temperature are the top three popular input features for thermal comfort prediction. Using different machine learning algorithms, data-driven thermal comfort models were reported to have a median predicting accuracy of 84% and some of them can predict thermal comfort at a personal level. Cases implementing occupant-centric thermal comfort control strategy were reported to save air-conditioning energy by 22% and improve thermal comfort by 29.1%. These observations from the literature support the prospects of the new thermal comfort paradigm. Additionally, the challenges and opportunities in this emerging field were discussed.

Huang G., Li D., Ng S.T., Wang L., Zhang Y.

Liu Y., Dang R., Yang B., Liu P.

Berkani M.R., Chouchane A., Himeur Y., Ouamane A., Miniaoui S., Atalla S., Mansoor W., Al-Ahmad H.

Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, and FedMA, and diverse innovative applications in thermal comfort optimization, energy prediction, healthcare, and anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy and robust performance across heterogeneous environments. We further discuss the integration of FL with advanced technologies, including digital twins and 5G/6G networks, and demonstrate its potential to revolutionize real-time monitoring, and optimize resources. Despite these advances, FL still faces challenges, such as communication overhead, security issues, and non-IID data handling. Future research directions highlight the development of adaptive learning methods, robust privacy measures, and hybrid architectures to fully leverage FL’s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings.

Lu S., Zhou S., Ding Y., Kim M.K., Yang B., Tian Z., Liu J.

Haque E., Hasan Z.

Qaisar I., Liang W., Sun K., Xing T., Zhao Q.

Liu G., Gao J., Han Z., Yuan Y.

Hosamo H., Mazzetto S.

This paper explores innovative approaches to reducing energy consumption in building ventilation systems through the implementation of adaptive control strategies. Using a publicly available high-resolution dataset spanning a full year, the study integrates real-time data on occupancy, CO2 levels, temperature, window state, and external environmental conditions. Notably, occupancy data derived from computer vision-based detection using the YOLOv5 algorithm provides an unprecedented level of granularity. The study evaluates five energy-saving strategies: Demand-Controlled Ventilation (DCV), occupancy-based control, time-based off-peak reduction, window-open control, and temperature-based control. Among these, the occupancy-based strategy achieved the highest energy savings, reducing power consumption by 50%, while temperature-based control yielded a significant 37.27% reduction. This paper’s originality lies in its holistic analysis of multiple dynamic control strategies, integrating diverse environmental and operational variables rarely combined in prior research. The findings highlight the transformative potential of integrating real-time environmental data and advanced control algorithms to optimize HVAC performance. This study establishes a new benchmark for energy-efficient building management through offering practical recommendations and laying the groundwork for predictive models, renewable energy integration, and occupant-centric systems.

Sun Z., Sierra F., Booth C.A.

Higher Education Institutions (HEIs) are an indispensable part of the UK society. In 2011, the Higher Education Funding Council for England set a carbon reduction target of 43% by 2020, that most HEIs failed to meet. HEIs have a clear interest in improving student experience/comfort and their sustainability credentials. However, this research found that space management in HEI is clearly inefficient. This research represents the first phenomenological study in this field and identifies current practices in HEIs regarding their space/energy management. First, a traditional thematic literature review was completed, which found a clear gap on what data is collected and used to calculate the ventilation/heating/cooling by facilities managers at HEI. Then, semi-structured interviews were conducted with facilities management experts (n=10) based in HEIs. This study concludes that HEIs should calculate ventilation, heating and cooling loads working in geometry and real occupancy, as this will improve occupant comfort, enrich the student experience and increase energy efficiency. A building information modelling (BIM) platform could be adopted to integrate different systems so that building management systems (BMS) have enhanced access to time-sensitive space/energy data.

Zhang S., Zhang X., Jiang J., Ma L., Lin Z.

Zhou B., Li Z., Yang B., Wang F., Ai Z., Melikov A.

Haifeng L., Hou H.C., Gou Z.

Liang Y., Yang J., Zhong Z., Xie Y., Dharmasastha K., Niu J.

Wu Y., Jiang H., Chen W., Fan J., Cao B.

Wang Z., Tang H., Zhang H., Jiang D., Leng Y., Wu Y., Calautit J., Xia L., Chaudhry H.N.