Thermal Comfort Of Occupants Research Articles

Impact of space utilization and work time flexibility on energy performance of office buildings

This study investigates the impact of different space utilization on energy use intensity, heating load, cooling load, and thermal comfort of occupants using a combination of empirical data gathering and simulation-based studies. The increasing worldwide preoccupation with energy consumption and environmental sustainability has led to increased attention on optimizing interior spaces to mitigate total energy demand. The considered case study for conducting this research was the Norwegian living lab namely Zero Emission Building (ZEB) Flexible Lab in Trondheim, Norway. In this study, 10 different scenarios of occupancy schedules based on flexible arrangements from standard workweeks to extensive remote work configurations were designed and analyzed using IDA ICE 5.0. The findings demonstrate significant reductions in energy use across scenarios with increased teleworking and compressed work weeks. The remote scenario achieved the most significant decrease in Energy Use Intensity (EUI), with a reduction of 46 % compared to the base case. Similarly, the implementation of flexible hours and remote working in scenarios resulted in a reduction of electric heating demand by up to 23 %, underscoring the potential of occupancy-based strategies in enhancing building energy efficiency. However, uncomfortable hours increased by 59 % in the 2-day remote working scenario compared to the base case, demonstrating the need to consider climate conditions when implementing remote work. The research offers valuable insights into the complex connections between flexible arrangements and energy efficiency, considering many elements such as occupancy schedule and use dynamics. This article offers a comprehensive analysis that may give architects, building managers, and policymakers valuable insights. This study contributes to the developing sustainable architecture by emphasizing the impact of dynamic occupancy on the energy performance of office buildings.

Bibliometric analysis and literature review of occupant thermal comfort in naturally ventilated buildings (1995-2021).

This paper presents the global research landscape and scientific progress on occupant thermal comfort in naturally ventilated buildings (OTC-NVB). Despite the growing interest in the area, comprehensive papers on the current status and future developments on the topic are currently lacking. Hence, the publication trends, bibliometric analysis, and systematic literature review of the published documents on OTC-NVB were examined. The search query "Thermal Comfort" AND "Natural Ventilation" AND "Buildings" was designed and executed to recover related documents on the topic from the Elsevier Scopus database. Results showed that 976 documents (comprising articles, conference papers, reviews, etc.) were published on the topic from 1995 to 2021. Further analysis showed that 97.34% of the publications were published in the English language. Richard J.de Dear (University of Sydney, Australia) is the most prolific researcher on OTC-NVB research, while Energy and Buildings has the highest publications. Bibliometric analysis showed high publications, citations, keywords, and co-authorships among researchers, whereas the most occurrent keywords are ventilation, natural ventilation, thermal comfort, buildings, and air conditioning. Systematic literature review demonstrated that OTC-NVB research has progressed significantly from empirical to computer-based studies involving complex mathematical equations, programs, or software like artificial neural networks (ANN) and computational fluid dynamics (CFD). In general, OTC-NVB research findings indicate that physiological, social, and environmental factors considerably influence OTC in NVBs. Future studies will likely employ artificial intelligence or building performance simulation (BPS) tools to examine relationships between OTC and indoor air/environmental quality, human behavior, novel clothing, or building materials in NVBs.

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

As space heating accounts for 54% of annual residential electricity consumption in Quebec, demand response programs specifically target load shifting through the automated control of thermostat setpoints during peak hours. On a district scale, varied thermostat preferences and setpoint override behaviors can have an impact on the success of the demand response program. This study examines two unique occupant types (Average, Tolerant) in terms of thermostat setpoint preferences as well as three different occupant Levels-of-Detail (LoDs) and analyzes their effects on the energy flexibility provided during demand response periods. For our baseline scenario, LoD 1, a static setpoint schedule is used and there is no control of the heat pump, while LoD 2 and LoD 3 incorporate thermostat setbacks during demand response events. LoD 2 assumes the occupant is comfortable within 2°C from the setpoint while LoD 3 allows the occupant to override the DR setbacks. We estimate the flexibility services provided by a ten-house residential community through the automated control of heat pumps during a three-month winter period, and we implement and simulate our study in CityLearn using reinforcement learning-based control for district-level energy management. When comparing LoD 3 to LoD 1, electricity cost was reduced by approximately 12% and net electricity consumption was reduced by approximately 17% during demand response periods. Likewise, we find that LoD 2 could overestimate savings in net electricity consumption, cost, and peak demand by 5% compared to LoD 3. The number of hours where the indoor temperature deviated more than ▪ from the setpoint occurred for less than 5% of the timesteps on average for the 10 buildings for LoD 3 while still achieving significant net electricity consumption reductions, thus highlighting that we can provide energy flexibility services to the grid while balancing occupant thermal comfort. Finally, the agents learned optimal decision-making that reduced the number of overrides across the training episodes for both Average and Tolerant occupants. We thus present a multi-agent framework as a means for addressing various occupant setpoint preferences and override behaviors for the control of heat pump systems at the neighborhood level.

Field Study on Winter Thermal Comfort of Occupants of Nursing Homes in Shandong Province, China

Field Study on Winter Thermal Comfort of Occupants of Nursing Homes in Shandong Province, China

Evaluating annual thermal discomfort time ratio of indoor occupants caused by solar radiation using a novel model

Thermal comfort of indoor occupants exposed to solar radiation is receiving widespread attention. Researchers have proposed many models to predict solar radiation and related indexes are used to evaluate thermal comfort. However, there are some limitations in the existing solar radiation models and evaluation indexes, such as only applying to sunny weather and requiring intensive modeling work. This study adopts a mathematical model called the improved HNU Solar Model and proposes a new evaluation index called the annual thermal discomfort time ratio by solar radiation (ratiotd, solar) to evaluate thermal comfort of indoor occupants exposed to solar radiation. The effects of different window parameters, i.e. window direction, window transmittance (Tsol) and window-to-wall ratio (WWR) on ratiotd, solar were also analyzed. The results show that the indoor area less than 2.0 m away from the window is easy to have solar discomfort. And for every 0.1 reduction in WWR, the average values of the four directions are reduced by 3% to 4%; and for every 0.1 reduction in Tsol, the ratiotd, solar values of four window directions are reduced by 4% to 6%. This study provides references for evaluating and optimizing the window design to create thermally comfortable environments for indoor occupants.

Informing building retrofits at low computational costs: a multi-objective optimisation using machine learning surrogates of building performance simulation models

Machine learning (ML) algorithms are increasingly used as surrogates for building performance simulation (BPS) models to leverage their energy predictive capabilities while reducing computational costs. In parallel, researchers are developing optimisation methods to inform building design and retrofit strategies but rarely employ ML-based BPS surrogates for this purpose. This study proposes a coupled modelling approach that leverages the capabilities of ML-based BPS surrogate models and multi-objective optimisation to inform holistic design and operation retrofits at low computational costs. The proposed methodology is demonstrated using an archetypal office building in Ottawa, Canada. The developed models achieved competitive predictive accuracies (adjusted R2: 0.90–0.99), identifying total and peak energy saving measures with up to 34% improvement in occupant thermal comfort at computational speeds 1266 times faster than a traditional BPS-based optimisation approach. Results offer a promising modelling workflow for design applications requiring extensive computations and scenario analyses, such as net-zero energy retrofits.

Investigating enhanced thermal comfort and energy efficiency through strategized airflow in Micro-Zonal Occupant-Centric Control (MZOCC)

The potential of Micro-Zonal Occupant Centric Control (MZOCC) to save HVAC energy consumption in medium sized open-plan offices has been established in literature. This is done by virtually dividing a thermal zone into micro-zones and allowing independent diffuser-level control in each micro-zone when the micro-zone is occupied. In continuation, there is a need to ensure that thermal comfort is achieved within micro-zones under such localised control. This is important because several forms of discomfort such as draft, horizontal thermal asymmetry and vertical thermal gradients may arise in MZOCC. Further, the effect of MZOCC airflow control strategies, such as setback flow and setback temperature, which were proposed in previous studies on thermal comfort of occupants needs to be evaluated. In an attempt to address these research gaps, this study explores thermal comfort for different scenarios of diffuser arrangements, airflow control strategies and occupancy conditions and also explores the effect of varying supply velocities used in setback flow and setpoint temperatures used as setback temperatures on air distribution and thermal comfort.Results show that low PMV values, increased draft discomfort and heavy thermal gradients are observed for certain scenarios of MZOCC. Tuning airflow control by choosing appropriate setback flow or setback temperature helps in restoring thermal comfort. As different forms of discomfort are observed in different cases, it is important to plan micro-zones and control airflow, such that, MZOCC does not violate thermal comfort criteria while optimising energy efficiency. This study gives a framework for selecting appropriate airflow control strategies for a selected diffuser arrangement for a given occupancy condition. Comfort-based MZOCC that uses appropriate airflow control strategy saves about 60% energy when the zone is half occupied and about 85% energy when the zone is quarter occupied. Results also show that, improving the diffuser arrangement improves energy savings and increases the options of airflow control strategies that can be used. Also, different airflow control strategies behave differently for each diffuser arrangement and has to be chosen separately for given diffuser setting and occupancy condition.

Multi-objective optimization of energy and thermal comfort using insulation and phase change materials in residential buildings

Recently, due to the increasing demand for energy and the identification of fossil fuels and their pollution, the focus on renewable energy is increasing day by day, so the use of renewable energy and energy-saving techniques is very important. PCMs are one of the most effective means of thermal energy storage, whose task is to change phase with increasing temperature, store thermal energy, and release this energy with decreasing temperature. This is one of the applications of these materials in building walls. In this research, the aim is to analyze and investigate the use of phase change materials in addition to thermal insulation. For this purpose, optimization in terms of energy and thermal comfort of residents has been done by the response surface method. Four Iranian cities with different climates—Tehran, Bandar Abbas, Tabriz, and Rasht—have undergone this optimization. According to the results, it can be concluded that the best temperature for the heating thermostat is approximately 20 °C and for the cooling thermostat, approximately 28 °C and 25 °C. Also, the best thermal insulation and PCMs are polyurethane and BioPCMDSCM27Q21. The thickness of the thermal insulation in the range between 6.9 cm and 9.8 cm is chosen as optimal, and the thickness of the phase change material is approximately 5 cm optimal for all cities (except Tehran). Due to the optimization of thermal comfort between 25 and 60 %, heating electricity between 43 and 99 % and cooling electricity between 38 and 52 % have been improved.

OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our OcAPO system can assure ambient temperature experienced by occupants within a tolerance of 0.3°C. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2∘ under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based OcAPO approach is able to assure occupant-level ambient temperature within a narrow 0.2°C tolerance. We also demonstrate that the regression version of OcAPO can reduce the opening percentage of PDC valves (an indirect proxy for energy consumption) by 58.9% under low occupancy compared to the trace-based model.

Thermophysiological responses and thermal comfort of occupants in indoor spaces under different speaking and non-speaking conditions

As speaking is one of the fundamental behaviours in human activities, understanding the thermal comfort of occupants during speaking is crucial to providing a satisfactory environment. To further strengthen this understanding and identify how occupant thermal responses change during speaking activities, this study performs an experiment to examine the thermophysiological parameters and thermal comfort of occupants across three different speaking and non-speaking conditions: (1) silent (i.e., non-speaking); (2) speech (i.e., continuous speaking); and (3) conversation (i.e., intermittent speaking). During the experiment, thermophysiological parameters, including skin temperature, electrodermal activity, and heart rate, were collected using smart wristbands from 30 subjects at three different temperature settings to represent three distinct thermal environmental conditions: (1) Cold: 20 °C; (2) Neutral: 24 °C; and (3) Hot: 28 °C. Subjects were also required to report their thermal comfort perceptions in terms of thermal sensation votes, thermal preference votes, and thermal satisfaction votes. Repeated measures ANOVA test and Friedman test were used to analyse the thermophysiological parameters and thermal comfort perceptions. The null hypothesis, which suggests no significant differences in thermal responses and comfort across the three speaking conditions, will be rejected at a significance level of 0.05 (two-tail). The results revealed significant differences in all three thermophysiological parameters between speaking conditions at all temperature settings, while the thermal comfort perception did not consistently show statistically significant differences between speaking conditions. The findings contribute to our understanding of the relationship between communication and thermal responses, highlighting the importance of contextual factors in designing comfortable environments for speaking-related activities.

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Investigating the effect of lower body local radiant warming on occupant thermal comfort in battery electric vehicles during cold conditions

The wintertime decrease in the driving range of battery electric vehicles (BEVs), partially attributed to cabin heating with improper setpoints, necessitates the improvement of occupant thermal comfort with reduced energy consumption. While local warming shows promise, understanding its impact on occupant comfort with thermal transients from cabin heating, ventilation, and air conditioning (HVAC) is limited. To address this gap, we investigated thermal perception in twenty-five participants under three distinct warming modes within BEVs operating through typical winter conditions: HVAC alone, HVAC with continuous lower body local radiant warming (LRW), and HVAC with periodic lower body LRW. Results show that the rapid change in skin temperature from lower body local warming does not immediately improve comfort. While a significant improvement in comfort (p < 0.05) is observed with lower body warming, it becomes evident only when the cabin transients subside. Consequently, we propose a framework that advocates for reducing HVAC setpoint and control of LRW of the lower body based on occupant preferences. Our feasibility study demonstrates a 45.3 % reduction in energy consumption by reducing the HVAC setpoint from 25 to 18 °C. These findings enhance the prospects for efficient thermal comfort management in BEVs.

Insulation performance improvement for drywall using highly insulated steel studs with slots

Drywall systems are a highly constructible technology with short construction times that allow the recycling and reuse of materials. However, steel studs used to anchor the insulation can cause thermal bridging owing to their high thermal conductivity, which can reduce the insulation performance and thermal comfort of occupants. This study proposes a new design method for drywall systems that can overcome the shortcomings and contribute to building energy conservation. Studs with slots applied in the web area are proposed, and the thermal performance and optimal slot size were determined by thermal analysis simulations. Through sensitivity and regression analysis, a design method for drywall was proposed. The developed studs showed an increase in insulation performance of approximately 22.1 % compared with conventional studs. The error rate between the simulation results and the regression model was 0.4 %. An experiment was conducted to verify the insulation performance of the studs. The results of this study can contribute to expanding the application of drywall technology in the building field by solving the problem of thermal bridging and providing a unique perspective on the drywall.

Influence of Thermal Comfort on Energy Consumption for Building Occupants: The Current State of the Art

Thermal comfort is a complex issue in the built environment due to the physiological and psychological differences of each individual in a building. There is a growing worry over the environmental implications of energy use as a result of the warming of the global climate and the growth in the number of instances of extreme weather events. Many review articles have been written, but these reviews have focused on a specific aspect of occupant behavior and thermal comfort. To research the trends of thermal comfort and energy, this research adopted mixed reviews, i.e., quantitative and qualitative, to understand the state-of-the-art factors affecting the thermal comfort of occupants concerning energy, different occupant modeling approaches, functions, and limitations. The in-depth qualitative discussion provides deeper insights into the impacts of occupant behaviors, factors affecting thermal comfort, and occupant behavior modeling approaches. This study classified occupant behaviors into five categories: occupant characteristics, perceptions of the occupant, realistic behaviors, heat gain, and occupant interactions with the system. It also went further to classify the factors affecting the thermal comfort of users based on past works of literature. These include structural, environmental, and human factors. It was concluded that factors that have the most significant impact on energy are human, structural, and environmental factors, respectively. In addition, most of the occupant behavior modeling approaches that have been used in past studies have pros and cons and cannot accurately predict human behaviors because they are stochastic. Future research should be conducted on thermal comfort for different building functions by examining the varied activity intensity levels of users, especially in educational or commercial buildings. Additionally, a proper investigation should be carried out on how thermal insulation of structural members influences thermal comfort. These should be compared in two similar buildings to understand occupant behavioral actions and energy consumption.

Occupant's thermal comfort augmentation and thermal load reduction in a typical residential building using genetic algorithm

The uncontrollable rise in energy consumption become a most significant issue in recent decades. One of the largest consumers of energy resources across all industries is the residential building sector. Researchers have suggested several strategies to reduce energy loss, including enclosing insulation in wall structures because air conditioning systems account for the majority of energy use inside homes. The main goal of this article is to increase residents' thermal comfort (Tc) while reducing their heating load (HL) and cooling load (CL). Using the EnergyPlus program, the building model was simulated in sample cities with various climatic conditions. For optimization, the first seven design variables were determined in Jeplus software and then multi-objective optimization was performed by the Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm. As a result, Tc, HL, and CL values improved by 38–62, 61 to 100, and 17 to 39 percent, respectively.

Occupant behavioral adjustments and thermal comfort with torso and/or foot warming in two cold indoor environments

Personal warming systems have been shown to improve occupant thermal comfort in cold indoor environments. Currently, there are almost no published comprehensive studies on the examination of occupant behavioral patterns in relation to the use of personal warming systems. Thus, this study presented a human trial study to investigate the effects of torso and foot warming on thermal comfort of young female occupants in two cold indoor environments (15 °C and 17 °C), based on a water warming suit (WWS) and HYB (a WWS combined with heated socks). Besides, occupant behavior adjustments regarding water temperature setpoints for WWS and electrical heating levels for heated socks were thoroughly investigated. WWS and HYB improved occupant thermal comfort at 15 °C and 17 °C, with ≥85.7 % of ‘No Change’ OTPVs (overall thermal preference votes) observed during the last 20 min of exposure. Foot heating can significantly reduce heating demand on the upper torso, both in terms of water setting temperatures and total heating time. HYB exhibited a 25.4 % and 43.3 % shorter total duration with water setting temperatures of ≥40 °C compared to WWS at 15 °C and 17 °C, respectively. Further, occupants have employed a trial-and-error adjustment approach to determine the heating temperature/level that offers optimal thermal comfort in the cold. Lastly, the use of two PWS combinations has the potential to reduce energy consumption for indoor heating by 62.9 %, 32.1 %, and 53.7 % in cities such as San Francisco, Phoenix, and Minneapolis (baseline heating setpoint: 21.5 °C).

Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

Prediction of occupant thermal state via infrared thermography and explainable AI

Accurate and real-time assessment of occupant thermal comfort can provide a solid foundation for efficient air conditioning operations. Existing studies already show the feasibility of using contactless technologies for thermal comfort prediction assisted by machine learning algorithms. However, the lack of transparency in machine learning often weakens user trust. This study performs explainable AI analysis to explore the potential of infrared imaging in thermal comfort evaluation. Specifically, an investigation was carried out in a climatic chamber, and infrared cameras were used to collect facial temperature data. Five popular ensemble tree models were employed to construct prediction models, and explainable AI analysis was performed using SHAP (SHapley Additive exPlanations) theory. Results show that combining additional facial information can significantly improve the overall model performance, and certain facial attributes present high contributions based on SHAP values. Combining facial features with explainable AI provides a convincing basis for thermal comfort assessment. The high SHAP values of facial features can also contribute to finding selective occupants with low neutral voting rates, providing evidence for customized cooling or heating from building systems.

Energy consumption and thermal comfort assessment using CFD in a naturally ventilated indoor environment under different ventilations

The rising energy demands of modern buildings necessitate the proper balance between occupant comfort and energy efficiency. This study numerically evaluates the effect of different inlet and exhaust vent profiles on indoor IEQ and energy consumption in well-validated, occupied office buildings for a cold climate conditions. Using ANSYS products, the research evaluates the impact of different ventilation configurations on occupant comfort using different comfort parameters such as, PMV, PPD, air temperature, temperature gradient, fluid flow velocity. The study also analyze how different ventilation configurations affect the thermal load on the radiator for indoor thermal comfort. The findings reveal a significant effect of ventilation profiles on both IEQ and energy consumption. Inadequate ventilation configuration selection can lead to a significant increase in radiator heat load, potentially increase up to 117.3%. Conversely, the adoption of an optimized ventilation strategy can simultaneously reduce energy consumption and improve indoor occupant thermal comfort. Indoor occupant comfort with minimal radiator heat load can be achieved using vertically oriented, rectangular inlet and exhaust vents with a perimeter ratio of 0.611. This research contributes to the development of sustainable buildings and describes indoor comfort design for cold climate conditions.

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.