Comfort Of Building Occupants Research Articles

Integrating Internet of Things (IoT) Approach to Post-Occupancy Evaluation (POE): An Experimental At-the-Moment Occupant Comfort Control System

This paper describes an empirical experiment of Internet of Things (IoT)’s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants’ control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT’s enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings.

WiSA: Privacy-enhanced WiFi-based activity intensity recognition in smart buildings using personalized federated learning

The increasing demand for enhanced energy efficiency and occupant comfort in buildings has led to a focus on developing occupant-centric building control systems. A key aspect of this study is to obtain real-time and accurate information related to occupants' activities. This study presents WiSA, an innovative, WiFi-based framework for recognizing the intensity of occupants' activities. WiSA offers non-intrusive, personalized activity recognition while minimizing privacy risks through the use of federated learning. We developed a specialized deep learning neural network for classifying activities into different intensity levels (light, moderate, vigorous), incorporating a tailored fine-tuning strategy to address the complexities of federated learning. Our dataset, exhibiting non-identically and independently distributed (No-IID) characteristics, was derived from the activities of 15 occupants in two rooms. This dataset was instrumental in demonstrating WiSA's effectiveness. The results show that WiSA achieved an impressive 98.0% average accuracy across all clients without necessitating the upload of annotated local data. The personalized fine-tuning strategy enhanced the performance of federated learning models by an average of 18.7%, markedly increasing adaptability to No-IID data. Additionally, WiSA displayed substantial robustness in handling the resolution of CSI frames. This approach offers a privacy-conscious method for building managers to gather information on occupant activities, enabling the development of more effective building operation strategies.

Improving building occupant comfort through a digital twin approach: A Bayesian network model and predictive maintenance method

This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems’ behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study’s results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings.

A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)

Occupant comfort in buildings is one of the most crucial considerations in designing a building. Accordingly, there is a growing interest in this area. Aspects of comfort include thermal comfort, visual comfort, acoustic comfort, and indoor air quality (IAQ) satisfaction. The objective of this state-of-the-art review was to provide a comprehensive, explicit, and up-to-date literature review on occupant comfort in buildings, since this issue has a great impact on the lifestyle, health, and productivity of occupants. A meta-synthesis method was also used for an analytical-interpretive review of previous studies. In this research, scientific research studies related to the subject of indoor occupant comfort in the period 2002–2022 were reviewed. Previous reviews have often covered the fundamental concepts and principles related to indoor occupant comfort. Although innumerable studies have focused on thermal comfort, other aspects of occupant comfort have not been considered. The review is analyzed and discussed in reference to type of study, case study geographical locations and climate zones, case study building types, decision-making models, assessment criteria, data-collection tools, and data analysis strategies. Finally, future research recommendations are presented. Through the review, we find that the comfort models used in research are mostly based on comfort perception votes collected from experimental studies, which may not reflect the preferences of users well. In addition, only the influence of environmental factors on the models has been investigated, and other personal factors have been ignored. This study presents a useful guide for researchers to determine their outlines for future research in this field.

Performance and economic efficiency analysis of an integrated, outdoor fan-ventilated cooling device

Recently, the importance of mechanical facilities in charge of the safety and comfort of occupants in buildings has once again been highlighted in accordance with global social issues such as the spread of COVID-19. In response, various ventilation systems are being developed to improve indoor air quality, and efforts are being made to satisfy the indoor comfort of the occupants. Such advanced facilities allow occupants to secure indoor air quality, while frequent ventilation systems can affect the cooling and heating load in the building, and there is also a problem that it can occupy a relatively large amount of space in the building. This study proposes an integrated, outdoor fan-ventilated cooling device and analyzes its performance and economic efficiency. The EnergyPlus simulation program was used to model two types of systems for comparison: an existing (base) model with a condenser located in the outdoor unit, and a developed model with the condenser integrated within the cooling system. The state of the air passing through the condenser was analyzed prior to comparing the efficiency of the integrated, outdoor fan-ventilated cooling device, followed by an in-depth analysis of the performance and economic efficiency based on total energy consumption. In Case 1, the air passing through the cooling system was approximately 5 °C lower than the base model and showed 11% peak load reduction in comparison to the maximum energy consumption. Additionally, a comparison between regions with different outdoor air temperatures showed an average cost reduction of 16% in Daejeon and Busan City.

Theoretical Minimum Thermal Load in Buildings

Summary Building cooling and heating accounts for a large portion of total global energy use and requires commensurate amounts of resources, which contribute significantly to global warming. Traditionally, addressing this issue has meant improving the efficiency of equipment supplying the thermal energy, reducing envelope heat transfer, and reducing air infiltration. However, this approach is already reaching practical limits. In this perspective, we explore (1) how to reduce thermal load in buildings theoretically and (2) how to achieve that reduction and dramatically lower the energy required to support building loads practically. First, we discuss our framework developed for calculating the theoretical minimum thermal load (TMTL) in buildings. Our analysis shows that current thermal loads in buildings are more than an order of magnitude higher than the TMTL. We also introduce an approximate formula to calculate energy savings from zonal control of thermal load, which shows that the majority of zonal control benefits can be achieved with fewer than 10 zones. Then, we discuss pros and cons of various approaches and strategies to achieve the TMTL. We conclude our perspective with some longer-term R&D ideas, such as thermally adaptive clothing and thermal storage to help approach the TMTL, while providing the additional benefit of interacting with the renewable grid of the future.

Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort

Buildings consume tremendous energy for the improvement of living and working conditions. Control of daylight-artificial light has the potential to improve energy performance and occupant comfort in buildings. This research proposes an intelligent generalized ensemble learning technique to develop a novel control strategy for Venetian-blind positioning (up-down movement with static slat angle of 45°) of different window orientations. The proposed model helps to maintain occupant comfort and energy saving in a commercial building. The performance of the ensemble learning approach compared against Gaussian process regression, support vector regression and artificial neural network using conventional statistical indicators. Finally, the proposed data-driven model implemented in a real-time Labview-myRIO platform for the experimental validation. The data-driven model is compared with the baseline model and with the uncontrolled blind condition in terms of daylight glare, and energy consumption of lighting and air-conditioning system in the building. The data-driven model is derived using two years of data collected from a fuzzy-based daylight-artificial light integrated scheme. The blind position providing reduced energy consumption and daylight glare along with setpoint illuminance and temperature are validated. A high dynamic range image with EVALGLARE software used to verify the visual comfort based on daylight glare probability. While evaluating the overall energy savings, the ensemble learning model consumes 17% less power than the uncontrolled system and 15% less power than the baseline system. Here, though we are not controlling the air-conditioning system, the experimental validation confirmed that the air-conditioning system significantly reduces its energy consumption.

IMPLICATION OF INFILTRATION ON BUILDING ENERGY DEMAND - A REVIEW OF VERIFICATION METHODS

<jats:p>Infiltration has a considerable impact on both, energy efficiency and occupant comfort in buildings. Due to the complexity of the analysis of this phenomenon in buildings, the verification methods are very important for its diagnostics and evaluation. In this paper, the matter of infiltration in buildings is being considered referring to both, calculation models and methods, as well as through current standards and regulations in the EU and Serbia. Different valorization methods are presented and analyzed regarding their characteristics, applicability, and complexity. Finally, preliminary infiltration measurements with a pressurization test, conducted on selected buildings of Belgrade housing stock are presented and compared with values defined by the current regulations in Serbia. Results pointed out current problems and the need for improvements regarding the treatment of infiltration in local regulations and practice.</jats:p>

Get the picture? Lessons learned from a smartphone-based post-occupancy evaluation

In the past several decades, it has been widely recognized that psychological aspects, including perceived control over one’s indoor environment, are important to holistic building occupant comfort and satisfaction evaluations. However, current post-occupancy evaluations tend to focus on collecting quantitative data, despite overwhelming evidence that subtle qualitative factors can profoundly impact the quality of occupants’ adaptive opportunities and comfort. This paper presents the development and demonstration of a novel smartphone-based survey that is used to concurrently collect: 1) office occupants’ subjective evaluation of usability and comfort of spaces, 2) participant-sourced photographs of electrical and mechanical systems for improving occupant comfort, and 3) open-ended questions about the uploaded photos. This paper is primarily focused on methodological aspects of the photograph-based smartphone post-occupancy evaluation, while providing select results of a pilot study involving 41 participants in Canada. The photographs yield significant explanatory power and contextual insights for quantitative results, though with significant post-processing effort and a reduced sample size.

Predicting plug loads with occupant count data through a deep learning approach

Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-h predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next 8 h. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%–6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.

The effectiveness of adding horizontal greening and vertical greening to courtyard areas of existing buildings in the hot summer cold winter region of China: A case study for Ningbo

The “Hot Summer Cold Winter” climate region of China is one of the most challenging regions for providing occupant comfort in buildings, with high demands for heating as well as cooling, together with high humidity levels. Although the performance of buildings has improved with the implementation of the new Chinese National Building Standards for the Zhejiang Province, it is believed that adding passive strategies could help to alleviate the problem even further. This paper aims to investigate the effects of adding horizontal greening (green roof) and vertical greening in the courtyard area to existing public buildings in the “Hot Summer Cold Winter” climate region of China. Using computer simulation and measurements from a monitored case in the city of Ningbo, the effects of green retrofitting on human comfort and energy consumption are analysed. Simulation results suggest that with partial horizontal and vertical greening, the cooling load could be reduced by 8.8% and heating load by 1.85%. Comparisons between actual energy use before and after greening on the case building showed significant reductions in both winter and summer, confirming the positive effects of vertical and horizontal greening of public buildings in this region.

Review of the Current State and Future Development in Standardizing Natural Lighting in Interiors

Three elements mainly wind, water and sun seemed to determine in ancient ages the basic phenomena of life on Earth. Architectural history documented the importance of sun influence on urban and building construction already in layouts of Mesopotamian and Greek houses. Not only sun radiation but especially daylight played a significant role in the creation of indoor environment. Later, in the 20th century, a search of interaction between human life in buildings and natural conditions were studied considering well­being and energy conscious design recently using computer tools in complex research and more detail interdisciplinary solutions. At the same time the restricted daytime availability of natural light was supplemented by more efficient and continually cheaper artificial lighting of interiors. There are two main approaches to standardize the design and evaluation of indoor visual environment. The first is based on the determination of the minimum requirements respecting human health and visibility needs in all activities while the second emphasizes the behaviour and comfort of occupants in buildings considering year­around natural changes of physical quantities like light, temperature, noise and energy consumption. The new current standardization basis for daylight evaluation and window design criteria stimulate the study of methodology principles that historically were based on the overcast type of sky luminance pattern avoiding yearly availability of sky illuminance levels. New trends to base the daylight standardization on yearly or long­term availability of daylight are using the averages or median sky illuminance levels to characterise local climatological conditions. This paper offers the review and discussion about the principles of the natural light standardization with a short introduction to the history and current state, with a trial to focus on the possible development of lighting engineering and its standards in future.

Switchable photovoltaic windows enabled by reversible photothermal complex dissociation from methylammonium lead iodide

Materials with switchable absorption properties have been widely used for smart window applications to reduce energy consumption and enhance occupant comfort in buildings. In this work, we combine the benefits of smart windows with energy conversion by producing a photovoltaic device with a switchable absorber layer that dynamically responds to sunlight. Upon illumination, photothermal heating switches the absorber layer—composed of a metal halide perovskite-methylamine complex—from a transparent state (68% visible transmittance) to an absorbing, photovoltaic colored state (less than 3% visible transmittance) due to dissociation of methylamine. After cooling, the methylamine complex is re-formed, returning the absorber layer to the transparent state in which the device acts as a window to visible light. The thermodynamics of switching and performance of the device are described. This work validates a photovoltaic window technology that circumvents the fundamental tradeoff between efficient solar conversion and high visible light transmittance that limits conventional semitransparent PV window designs.

An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands

The increasing trends of energy demand and renewable integration call for new and advanced approaches to energy management and energy balancing in power networks. Utilities and network system operators require more assistance and flexibility shown from consumers in order to manage their power plants and network resources. Demand response techniques allow customers to participate and contribute to the system balancing and improve power quality. Traditionally, only energy-intensive industrial users and large customers actively participated in demand response programs by intentionally modifying their consumption patterns. In contrast, small consumers were not considered in these programs due to their low individual impact on power networks, grid infrastructure and energy balancing. This paper studies the flexibility of aggregated demands of buildings with different characteristics such as shopping malls, offices, hotels and dwellings. By using the aggregated demand profile and the market price predictions, an aggregator participates directly in the day-ahead market to determine the load scheduling that maximizes its economic benefits. The optimization problem takes into account constraints on the demand imposed by the individual customers related to the building occupant comfort. A case study representing a small geographic area was used to assess the performance of the proposed method. The obtained results emphasize the potential of demand aggregation of different customers in order to increase flexibility and, consequently, aggregator profits in the day-ahead market.

Prototype Smart Sensor Pengukur Suhu Untuk Sistem Monitoring Pada Smart Building Management System

Prototype Smart Sensor for Measuring Temperature Monitoring System Smart Building Management System. The sensor is an electronic device that is extremely important in the field of instrumentation and industrial control. The sensor is an input device in the measurement and control of an automation system. Device sensor has now developed into a Smart Sensor. Smart Sensor means a sensor to be more easily applied and integrated with other electronic devices, supporting the latest technology, size is getting smaller, and has a higher sensitivity value. In this study created Smart Sensor Smart Sensor Temperature is in the form of a Prototype. Prototype Smart Temperature applied in measuring and monitoring the temperature of a building. Building and monitoring system is called the Smart Building Management System (SBMS). SBMS has the goal of keeping a building can be regulated, controlled and monitored easily, quickly, effectively, economically and efficiently. Prototype Smart Sensor will measure and monitor the temperature inside the building. Temperature is a measure of a building needed to the building occupant comfort, energy saving, fire prevention and building maintenance. This research has succeeded in making a prototype Smart Temperature Sensors for measuring and monitoring the temperature of a server room. Temperatures were measured by an average of 20-22 0C. The result of this will be used as reference and evaluation for the team management system to manage the building and maintenance of industrial automation equipment.

Modeling and Analysis of a Variable Speed Heat Pump for Frequency Regulation Through Direct Load Control

This paper presents a dynamic model of a variable speed heat pump (VSHP) in a commercial building that responds to direct load control (DLC) signals, updated every 4 s, for the improvement of grid frequency regulation (GFR). The model is simplified for real-time simulation studies with the time horizon ranging from seconds to hours, but still sufficiently comprehensive to analyze the operational characteristics such as the heat rate and coefficient of performance. A variable speed drive-controlled induction motor model is also established for the adjustment of the VSHP input power. A dynamic model of an experimental room is then developed to estimate the effect of the DLC application to the VSHP on its indoor air temperature for two different cooling systems. Furthermore, small signal analysis is performed to evaluate both the transient response of the DLC-enabled VSHP and its contribution to GFR. Finally, with an isolated microgrid implemented with Matlab/Simulink, simulation studies demonstrate that the VSHP can be effectively exploited as the DLC-enabled load while still ensuring building occupant comfort and long-term device performance.

Creating Sustainable Building through Exploiting Human Comfort

As Abbe Laugier elaborated in his book “Essay on Architecture”, architecture was founded on simple nature to protect occupants from harsh external environments, and thus providing comfortable indoor is one of the fundamental roles of buildings. Also, comfort along with building energy use for the provision of comfort is a key consideration for sustainable buildings. Traditional comfort theories based on experimental research imply that improving comfort levels would result in increasing energy consumption in buildings. This paper argues that new comfort theory can greatly contribute to reduction in building energy use, while satisfying occupant comfort in buildings. This paper illustrates how new comfort model based on the adaptive principle was developed from a series of field studies, and proves that the application of adaptive comfort model to the operation of buildings can lead to energy savings. This paper concludes with design considerations for sustainable buildings in order to realize energy saving potentials of the adaptive model in practice.

The effectiveness of retrofitting existing public buildings in face of future climate change in the hot summer cold winter region of China

This paper aims to investigate the effects of retrofitting existing public buildings in the “Hot Summer Cold Winter” climate region of China, using simulation and a case study, to see how retrofitted public buildings perform in the face of climate change in the region. This area of China is one of the most challenging regions for providing occupant comfort in buildings, with high demands for heating as well as cooling, together with high humidity levels. The simulation results suggest that energy reduction of up to 40% with improvements to the building enclosure is possible by retrofitting current existing public buildings to the new Chinese National Standard for the Zhejiang Province.Requirements for combating possible increase in energy consumption from heating and cooling demands due to future climate change was also investigated, and it was found that by simply improving U-Values of the building envelope to the new national standards set for the Zhejiang Province, the effects of future climate change can be nullified up to the 2080s. Further improvements to having similar standards as developed countries such as England and Wales will have less significance and cannot nullify the effects of climate change from the newly retrofitted public buildings. However, this can be achieved by involving other energy-saving strategies and installing suitable energy-saving technologies for the region.

The potential impact of climate change on heating and cooling loads for office buildings in the Yangtze River Delta

Located in the ‘Hot Summer Cold Winter’ climatic zone of China, the Yangtze River Delta area is one of the most challenging regions for providing occupant comfort in buildings, and effects of climate change in the next 100 years will make further increase in energy consumption. This article uses climate change data from HadCM3 to generate test reference years (TRYs) compiled for 2020s, 2050s and 2080s under various future scenarios for the cities of Ningbo, Shanghai and Hangzhou. Simulations were then conducted to see if effects of climate change can be contained or even reversed with improvements in building standards, and it was shown that energy consumption can be significantly reduced with building improvements, even in the face of climate change.

Wireless sensor network performance metrics for building applications

Metrics are investigated to help assess the performance of wireless sensors in buildings. Wireless sensor networks present tremendous opportunities for energy savings and improvement in occupant comfort in buildings by making data about conditions and equipment more readily available. A key barrier to their adoption, however, is the uncertainty among users regarding the reliability of the wireless links through building construction. Tests were carried out that examined three performance metrics as a function of transmitter–receiver separation distance, transmitter power level, and obstruction type. These tests demonstrated, via the packet delivery rate, a clear transition from reliable to unreliable communications at different separation distances. While the packet delivery rate is difficult to measure in actual applications, the received signal strength indication correlated well with the drop in packet delivery rate in the relatively noise-free environment used in these tests. The concept of an equivalent distance was introduced to translate the range of reliability in open field operation to that seen in a typical building, thereby providing wireless system designers a rough estimate of the necessary spacing between sensor nodes in building applications. It is anticipated that the availability of straightforward metrics on the range of wireless sensors in buildings will enable more widespread sensing in buildings for improved control and fault detection.