Thermal Comfort Of Occupants Research Articles

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Ice slurry ingestion for enhanced occupant thermal comfort in warm/hot indoor environments: A comparative study with an energy-efficient desk fan

Ice slurry ingestion has been widely used as an effective pre-exercise cooling strategy for athletes to improve exercise performance while competing in the heat. Nonetheless, its potential to improve thermal comfort for office workers in various indoor environments has received little attention. In this study, two representative external and internal body cooling modalities, ice slurry ingestion (ICE) and desk fan (FAN), were used under free control in three warm/hot environments (30, 32, and 34 °C, RH = 50%). Throughout the heat exposure, the subjects' physiological responses (heart rate and local skin temperatures) were recorded, as well as their overall and local thermal perceptions. The subjects' usage behavior was recorded, including the fanning speed level and ice slurry ingestion rate. The results show that, when compared to the FAN condition, ICE resulted in significantly lower thermal sensation (decreased by 0.93 rating scale, p < 0.01) and sweat rate (38.3 ± 34.6 g/h, p < 0.01), as well as higher thermal comfort (increased by 1.17 rating scales, p < 0.05) at 34 °C. The total amount of ice slurry ingested by the subjects increased significantly with increasing air temperature, from 107.5 ± 64.1 g/h to 249.2 ± 84.1 g/h (p < 0.001). This study demonstrates the pronounced efficacy of ice slurry ingestion to sustain young female occupant thermal comfort at 30–34 °C indoor temperatures. Further research is still necessary to investigate the efficacy of ice slurry ingestion in higher indoor temperatures during extreme heatwaves, however.

A Systematic Review on the Studies of Thermal Comfort in Urban Residential Buildings in China

There have been fruitful publications on thermal comfort of urban residential buildings in China. However, there is a lack of reviews on this topic to perform a comprehensive analysis and find opportunities to meet occupants’ thermal comfort needs while improving building energy efficiencies. This paper addresses this issue by presenting a systematic review on the advancements in research on thermal comfort in urban residential buildings in China. Firstly, two common thermal comfort research approaches, i.e., field studies and laboratory studies, are discussed. Secondly, eleven main thermal comfort evaluation indicators are summarized. Finally, this paper analyzes the thermal comfort survey data from different researchers, discusses the impacts of adaptive behaviors on human thermal comfort, and provides recommendations for future research on urban residential thermal comfort. It was found that people have higher and higher requirements for their indoor thermal environment as time goes by, especially in the winter; the thermoneutral temperature is higher in warmer climate regions in the summer but lower in the winter than in colder climate regions; the thermoneutral temperature tends to increase with the indoor air temperature due to an adaptation to the indoor thermal environment. The outcomes of this paper provide valuable information on thermal comfort behaviors of urban residents in different climate zones in China, which can serve as a resource for the academic community conducting future research on thermal comfort and assist policymakers in enhancing building energy efficiencies without compromising the occupants’ comfort.

Research on Automotive Seat Heating Strategy Based on Objective Thermal Comfort Evaluation

Abstract Seat heating is an essential means to improve cabin occupant thermal comfort. How to set a reasonable seat heating strategy is very important to improve occupant thermal comfort and reduce vehicle energy consumption. Based on objective thermal comfort evaluation criteria, this paper analyzes three strategies: fixed heating power, fixed seat surface temperature, and intermittent seat heating. It explores the occupant thermal comfort characteristics under different strategies. It is found that the optimum heating power or optimum seat surface temperature to maintain occupant thermal comfort varies with the initial seat temperature. Compared with the two strategies of fixed heating power and fixed seat surface temperature, the intermittent seat heating proposed in this paper can make the human body reach the optimal state of comfort in a shorter period, and the power consumption is less as the seat heating is opened for a longer time. This intermittent seat heating strategy not only adapts to different low-temperature environments outside but also provides sufficient safety guarantees while improving human thermal comfort. It can provide some references for the design of car seat heating and research on passenger cabin thermal comfort.

Thermal-Adaptation-Behavior-Based Thermal Sensation Evaluation Model with Surveillance Cameras.

The construction sector is responsible for almost 30% of the world's total energy consumption, with a significant portion of this energy being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people's thermal comfort. In practical applications, the conventional approach to HVAC management in buildings typically involves the manual control of temperature setpoints by facility operators. Nevertheless, the implementation of real-time alterations that are based on the thermal comfort levels of humans inside a building has the potential to dramatically improve the energy efficiency of the structure. Therefore, we propose a model for non-intrusive, dynamic inference of occupant thermal comfort based on building indoor surveillance camera data. It is based on a two-stream transformer-augmented adaptive graph convolutional network to identify people's heat-related adaptive behaviors. The transformer specifically strengthens the original adaptive graph convolution network module, resulting in further improvement to the accuracy of the detection of thermal adaptation behavior. The experiment is conducted on a dataset including 16 distinct temperature adaption behaviors. The findings indicate that the suggested strategy significantly improves the behavior recognition accuracy of the proposed model to 96.56%. The proposed model provides the possibility to realize energy savings and emission reductions in intelligent buildings and dynamic decision making in energy management systems.

Performance evaluation of an occupant metabolic rate estimation algorithm using activity classification and object detection models

To create a comfortable indoor environment, the metabolic rate (MET), which affects the thermal sensation of occupants, needs to be reflected in real-time. Recently, methods employing computer vision techniques classify activities based on the pose of the body in images. However, these methods face challenges in determining the MET depending on the objects used, even with the same pose. Therefore, the objective of this study is to develop a MET estimation algorithm that can estimate various METs by integrating a pose-based activity classification model and an object detection model. To achieve this, an object detection model capable of detecting and classifying six regularly used objects indoors was developed, and a performance evaluation was conducted. The MET estimation algorithm was assessed through the implementation of a thermal control system, validating its applicability in experimental settings. As a result, the object detection model exhibited a real-time classification accuracy of 89%. Additionally, when evaluating the mode value over 15-s intervals, it demonstrated a classification accuracy of 100%. The algorithm exhibited a real-time estimation accuracy of 83% for the six METs and examining the mode value for 15-s intervals, it demonstrated a classification accuracy of 99%. This study thus confirmed the control capability of the proposed MET estimation algorithm and its potential for the estimation of various METs. The developed method can be used for the real-time estimation of occupant thermal comfort in indoor comfort-based control systems, contributing to the realization of a comfortable environment for occupants that protects their well-being.

Adjacent heat transfer characteristics and energy efficiency optimization in university classrooms under partial-space heating by a numerical model based on field test

Universities’ course timetabling arranged by lecture conditions could cause different occupation schedules in classrooms, resulting in intermittent heating conditions. The adjacent heat transfer contributes to heating energy consumption. A new course timetabling was needed to decrease intermittent heating energy consumption by the reallocation of the adjacent room heat transfer. A university building in Chengdu, China, was chosen to conduct field study and measurement. The dynamic heat transfer between interior walls and its effect on the indoor thermal environment and heating energy consumption were investigated by EnergyPlus. The results showed that: (1) the differences between the heat transfer in adjacent walls under different conditions could exceed 50 %. (2) Increasing the heating synchronization rate, extending the heating duration, and reducing the interval improved the indoor thermal environment and reduced heating energy consumption by 37 %. (3) The recommended timetabling was that the adjacent classrooms both have 4 h of lectures in the morning and afternoon to meet the course demand. Compared with the benchmark, the heating load and carbon emission could be reduced by 40 % and 2.44 kg/h, respectively. The significance of this study is to improve occupant thermal comfort and reduce energy consumption in university buildings through developing educational timetabling.

Analysis of an earth‐to‐air heat exchanger for enhanced residential thermal comfort

AbstractIt is well known that a massive demand for thermal comfort has resulted in a sharp rise in energy use for residential space heating and cooling. This article presents numerical simulations of a room coupled with an earth‐to‐air heat exchanger system operating at the climatic condition of 21.2497° N, 81.6050° E for both summer and winter seasons. The present study involves an estimate of monthly variations in soil temperature at different depths and a suitable geometrical arrangement of pipe is proposed. The thermal performance of the earth‐to‐air heat exchanger system in terms of the outlet air temperature and effectiveness has been calculated. It is shown that with an effectiveness of 0.77 in summer and 0.78 in winter, the present EAHE system reduces the ambient air temperature from 38 to 28.2°C in the summer and it rises from 13 to 22.9°C in winter, respectively. Finally, an improvement in the thermal comfort condition of the room exposed to the solar radiation has been examined at different time intervals and different heights of the room. During the summer the room temperature dropped by 4.2°C while in winter it rose by 5.2°C. Furthermore, the environmental effects of system have been evaluated, showing energy savings of 3.90 kWh/day during summer and 4.83 kWh/day during winter leading to a decrease in CO2 emissions of 6.13 kg/day in summer and 7.59 kg/day in winter.

Application of artificial intelligence control in the control system of cooling and heating energy stations

In order to understand the application of artificial intelligence in the control system of cold and hot energy station, the author proposes a research based on artificial intelligence control in the control system of cooling and heating energy station. The author first introduces artificial intelligence technology on the basis of heating and precision heating, relies on the ?Internet plus? and IoT platform, applies indoor temperature measurement technology, intelligent control and information docking technology, and establishes a heating system based on artificial intelligence technology. This system has improved the level of enterprise management, promoted the further development of heating and intelligent energy conservation, reduced energy consumption, reduced pollution, and achieved the need for precise heating of the pipe-line network. Secondly, combined with the application case of a district, this district is selected as the application pilot to describe the role and effect of this system in the Hydronics. The experimental results indicate that the heat consumption of the residential area is 27.07 W/m2, while the heat consumption of the comparison residential area is 28 W/m2. Through indoor temperature measurement, it was found that the average indoor temperature of the comparison residential area is 22?C. Therefore, the application of artificial intelligence heating systems has improved the thermal comfort of residents, bringing users a high quality heating experience while relatively reducing energy consumption. The intelligent application of the Hydronics can more accurately grasp the heating situation, timely and efficiently adjust the heating scheme, achieve heating balance, and improve the utilization efficiency of heat sources. At the same time, it can also effectively improve the troubleshooting ability of the hydronics, prevent safety risks, improve management efficiency, and will certainly provide more powerful support for the energy-saving society and environment-friendly society.

Improving indoor air quality and thermal comfort using a total heat exchanger ventilation system for an office building

Indoor air quality (IAQ) and thermal comfort affect occupant comfort and productivity. Thermal comfort supports productivity, while IAQ maintains occupant health. However, occupants are the main source of carbon dioxide, which is the main index for indoor pollutants in educational buildings due to many occupants gathering in one room. This study investigates the performance of applying a total heat exchanger (THX) for diluting carbon dioxide (CO2). Besides, the thermal comfort of the occupants has been evaluated extensively as the THX also reduces the fresh air temperature. An office area with 14 graduate students seated in a meeting was investigated. Questionnaires and field measurements were conducted simultaneously. Time-varying IAQ (carbon dioxide concentration) was assessed using computational fluid dynamics. The numerical simulation program also contained User Defined Function (UDF) based predicted mean vote (PMV) algorithms to determine occupant thermal comfort. The results indicated that without using THX, the CO2 concentration gradually rises until it reaches a maximum of 1400 ppm inside the room. This condition occurs because there is no air change between indoor and fresh air, mainly due to the lack of fresh air supply and reliance on split AC for circulation. With THX, the concentration could lower to below 1000 ppm. In addition, it also could make room temperature slightly lower, with the overall temperature average in this study being 24.5?C without THX and 24.1?C when THX is operated.

Optimal HVAC Energy and Regulation Reserve Scheduling in Power Markets

This paper considers the optimal scheduling of the hourly energy consumed and regulation reserve capacity offered by a Heating, Ventilation and Air-Conditioning (HVAC) system participating in a day-ahead electricity market. We formulate an Integrated hourly energy and regulation reserve Scheduling and Deployment (ISD) problem using high fidelity models of the HVAC and the building structure's thermal storage property. The ISD problem optimizes the hourly costs and benefits resulting from the power consumed, the regulation reserve capacity offered, the occupant thermal comfort utility, and the expected Intra-Hour Costs (IHCs) due to the HVAC's imperfect tracking of the regulation signal broadcasted by the Independent System Operator every few seconds. Addition of the IHCs to the objective function is the major innovation of this paper. It enables optimal HVAC scheduling to internalize expected regulation signal tracking error cost traded-off against incremental occupant thermal discomfort that may result from perfect tracking. The cost causation circle closes by noting that a high hourly regulation reserve offer may result in higher expected IHCs by increasing the associated tracking error and the incremental occupant discomfort. The paper's innovation is achieved by (i) estimating an analytic function of the hourly HVAC scheduling decisions and state variables that approximates the expected IHCs, and (ii) including this exogenously estimated analytic IHCs function in the objective of the hourly ISD optimization problem. Non-convexities and non-linearities introduced to the HVAC optimization problem resulting from high modeling fidelity and the inclusion of the expected IHCs, are addressed efficiently through piecewise convex relaxations that provide tight optimality bounds. Extensive numerical results are finally provided to demonstrate the applicability and performance of our ISD problem formulation on a realistic office building.

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Economic, Technical, and Environmental Benefits of Occupants’ Thermal Comfort-Based Heat Loads Participation in Demand Response Program

The electrification of heating and transportation systems is one of the objectives of developed countries to minimize CO2 emissions. This objective pushes distribution systems’ (DBs) operators to incorporate numerous high-power loads into low-voltage networks that were not designed for such loads. DBs’ reconfiguration and the loads’ flexible characteristics are two remedies exploited to overcome this issue. Heat pumps (HPs), as the most prevalent loads, can be managed by demand response programs (DRPs) to postpone the costly reconfiguration of distribution systems. HPs’ DRP participation affects indoor air temperature. If this is not accomplished reasonably, the occupants’ thermal comfort (OTC) will be compromised, and it will be impossible to convince them to continue contributing to DRPs. Based on the ASHRAE55 standard and an experimental building electrothermal model, this article presents a novel framework for determining the HPs’ DRPs participation. This framework ensures the OTC and optimizes the HPs’ DRP participation. The modified IEEE 33-bus network is employed as the test system to evaluate the proposed method. The simulation results confirm the usefulness of the proposed strategy to improve the technical and economic aspects of the network.

Field study of meeting thermal needs of occupants in old residential buildings in low-temperature environments using personalized local heating

The outdated structures and heating devices in old residential buildings can no longer functionally meet the thermal needs of users and require reasonable supplementary measures to meet the demand. Personalized local heating, owing to its thermal comfort and energy-saving performance, is a good solution for addressing the thermal needs of occupants. The objective of this study was to examine the influence of personalized local heating on the thermal comfort of occupants in old residential buildings under low-temperature heating. A field study was conducted with 16 participants in typical old residential buildings under different conditions from January to February 2023. The findings revealed that all personalized local heating methods could increase the overall thermal sensation of participants at background temperatures of 16 °C and 19 °C, but only a few of them were effective in enhancing the participants' overall thermal comfort. Regardless of the chosen localized heating method, the participants’ thermal acceptance vote varied over time. When using localized heating, there were differences in the body parts selected for heating among participants at 16 °C and 19 °C. The analysis showed that the corrective power of heating devices for human thermal comfort is related to the background environment temperature, with stronger corrective power shown at lower temperatures. Furthermore, as long as the problem of uneven local heating was effectively resolved, radiant devices were the preferred environmental conditioning devices. This study provides important guidance, both for improving thermal comfort in old residential buildings and for expanding the application of personalized local heating.

A personalized regression model for predicting thermal sensation based on local skin temperature in moderate summer conditions

Personalized thermal sensation models play a crucial role in ensuring occupant's thermal comfort satisfaction and improving building energy efficiency. However, an adaptive and accurate personalized model that can be easily implemented in real life is still challenging. This paper investigates the influencing factors of thermal sensation vote (TSV) and proposes a personalized regression model that only uses a single local skin temperature as the key indicator. A survey is conducted with forty subjects aging from 20 to 59 years old. The relationship among ambient temperature, skin temperatures, and subjective TSV is analyzed. The forehead temperature is recommended as the key indicator for prediction because it exhibits a strong correlation with ambient temperature and TSV, and it is easy to capture. Furthermore, the impact of individual characteristics on TSV is investigated. The proposed model effectively captures and compensates for individual differences by incorporating subjects' set point skin temperature and body fat percentage (BF%). The proposed model can be readily applied in real-life scenarios due to its minimal requirement for occupant’s feedback and its higher accuracy compared to other models. Specifically, it exhibits a significantly lower Root Mean Square Error (RMSE) of 15.8 %, 9.4 %, and 65.2 % compared to the Support Vector Regression (SVR) model Zhang's model and Zhou's model. Moreover, the proposed model showcases the lowest mean absolute error among the compared models. This approach of developing a personalized regression model based on local body temperature holds promise for future international ergonomic standard development.

Integrated assessment of buildings visual and thermal performance with translucent bricks

In response to the growing need to reduce building energy consumption while ensuring occupants’ visual and thermal comfort, the Building Energy Materials and Components Lab at Empa developed a high-performance translucent construction element based on aerogel-filled glass bricks. The translucence and thermal insulation characteristics of this brick allows the exploration of different configurations of opaque and transparent surfaces to improve the integrated visual and thermal performance of the indoor environment. This paper presents a workflow to simulate the building performance in order to investigate the effects arising when the mentioned bricks are embedded in the building envelope. Simulations were conducted on a reference building in a continental climate assuming different orientations and window-to-wall ratios. Results show a clear trade-off between minimizing the cooling demand and maximizing occupant visual and thermal comfort. The application of the brick on the northern façade exhibited the best trade-off in terms of energy and comfort.

The impact of socio-demographic factors on occupants’ thermal comfort and sensation: An integrated approach using statistical analysis and agent-based modeling

The thermal comfort and thermal sensation of occupants can significantly impact the overall performance of the building. However, very little is known about how occupants perceive or thermally respond to their building interior layouts or what factors affect their thermal comfort and sensation, particularly in low-income residential homes. Through a post-occupancy evaluation of 394 occupants from 190 households in Chittagong, Bangladesh, this study examines the socio-demographic factors (i.e., gender, age, and home ownership) that influence occupants' thermal comfort and sensation. The chi-square, Cramer's V, and Lambda tests were used to statistically analyze the impact of selected socio-demographic factors. The results showed that female occupants were significantly less satisfied than male occupants with their existing thermal environment. However, the study did not find any influence of age or home ownership on occupant thermal comfort. Furthermore, comprehensive agent-based modeling (ABM) was implemented to support the results of the statistical analysis of the influence of gender on occupants' thermal comfort levels. The ABM was designed based on the theory of planned behavior. As a simulation output, the ABM model stochastically estimated occupants' thermal comfort and sensation level in terms of the predicted mean vote. Similar to the statistical analyses, the results of the ABM also indicated better thermal comfort and thermal sensation for male occupants than for female occupants. The results of the study can serve as a guideline for better understanding the socio-demographic factors involved in the modeling or remodeling of occupant thermal comfort in developing economies.

Transfer Learning in the Transformer Model for Thermal Comfort Prediction: A Case of Limited Data

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building’s energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results.

Integrated daylight and thermal comfort evaluation for tropical passive gymnasiums based on the perspective of exercisers

This study measured and surveyed four passive community gymnasiums with open facades in a tropical climate to explore the relationship between occupant daylight, thermal comfort, and environmental indicators during exercise. A questionnaire survey was conducted on exercisers to evaluate their satisfaction. Compared with the daylight environment, the subjects were more satisfied the intervention mechanism of the daylight and thermal environment indicators for the comfort level of participants in their respective dimensions with the indoor thermal environment of the four test cases. Hereafter, linear regression was used to explore the influencing mechanisms of daylight and thermal comfort on environmental indicators, the result showed that illuminance uniformity and natural ventilation were the main factors affecting occupant comfort. In order to improve the accuracy of the prediction model, the radial basis function (RBF) neural network was introduced to analyze the influence weight of different indicators on integrated sunlight and thermal comfort (IDTC), and a prediction model was established. Compared to the linear model, RBF has improved the predictive fit for DC, TC, and IDTC by 0.42, 0.33, and 0.39, respectively. This study integrates the assessment of daylight and thermal comfort, emphasizing the effect of passive climate conditioning indoors in sports facilities. The findings of the study can provide guidance for the improvement of the environmental quality of sports facilities and the satisfaction of exercisers in hot and humid climate.

Thermal performance of cavities in 3DPC building façades

In the quest for sufficient and comfortable housing in South Africa, attention to building orientation and thermal transmittance principles has significantly improved thermal performance in local houses. Thermal simulation and monitoring of houses in the Joe Slovo Phase Three development in Cape Town confirms improved performance by solar radiation blockage and shading. This paper simulates the thermal performance of a housing unit in this development. The house was instrumented with thermocouples, and monitored from March to December 2013 by Sustainable Energy Africa in collaboration with the National Department of Human Settlements. Sustainable Energy Africa reported significant improvement when compared to a nearby traditional Reconstruction and Development Programme house that had been monitored during the same period. With the benefit of the monitored thermal data, complete drawings, and specification details of the Joe Slovo Phase Three unit, the current research modelled and calibrated a simulation model in DesignBuilder. DesignBuilder was selected given its accreditation status for Green Star Rating of buildings in South Africa. An additional motivation for investigating this unit was the availability of recorded weather data from the nearby Cape Town International Airport, captured in the DesignBuilder climate data base for 2013, which was assumed relevant in lieu of complete weather station data measured at the location of the housing unit. The study aimed to simulate the monitored temperatures in the house with acceptable agreement, and to investigate further potential improvement in occupant thermal comfort by alternative 3D-printed concrete walling developed by the authors. Systematic finite element analysis (FEA) iteratively solved cavity radiation and convection in wall cavities to calibrate thermal transmittance parameters for DesignBuilder. The FEA results acceptably simulated temperatures monitored in this physical wall experiment. The calibrated DesignBuilder model simulated the indoor temperatures of the monitored house with acceptable agreement, and predicted significant improvement in occupant thermal comfort if the walls were 3D-printed with a particular cross-sectional design.