Comfort Prediction Research Articles

Assessment and prediction of pedestrian thermal comfort through machine learning modelling in tropical urban climate of Nagpur City

Assessment and prediction of pedestrian thermal comfort through machine learning modelling in tropical urban climate of Nagpur City

Effect of Shock Absorber Friction on Vehicle Vertical Dynamics

<div>In order to efficiently predict and investigate a vehicle’s vertical dynamics, it is necessary to consider the suspension component properties holistically. Although the effects of suspension stiffness and damping characteristics on vertical dynamics are widely understood, the impact of suspension friction in various driving scenarios has rarely been studied in both simulation and road tests for several decades.</div> <div>The present study addresses this issue by performing driving tests using a special device that allows a modification of the shock absorber or damper friction, and thus the suspension friction to be modified independently of other suspension parameters. Initially, its correct functioning is verified on a shock absorber test rig. A calibration and application routine is established in order to assign definite additional friction forces at high reproducibility levels.</div> <div>The device is equipped in a medium-class passenger vehicle, which is driven on various irregular road sections as well as over single obstacles. For all tested road sections, a linear decrease of ride comfort in terms of specific relevant vertical objective values is found by increasing the friction force. This emphasizes a definite link between suspension friction and vertical vehicle body vibration, resulting in a negative impact on vertical ride comfort. However, the longitudinal vehicle body vibration is not significantly affected.</div> <div>The relevance of friction in terms of transmitting the energy associated with road unevenness to the chassis in the frequency range of the chassis’ natural frequency is found to be remarkably high on smooth roads, and still considerably high on bumpy roads. The chassis and wheel resonance frequencies are significantly friction-dependent due to the damper’s slip or stick states. The results obtained from smooth road tests demonstrate the practical relevance of accurately considering friction for the given suspension type in terms of vertical ride comfort prediction.</div>

Thermal comfort study of urban waterfront spaces in cold regions: Waterfront skyline control based on thermal comfort objectives

In waterfronts, thermal comfort is affected by climate and urban morphology. Existing studies on waterfront thermal comfort have mainly focused on warm and hot regions, focusing on the cooling effects of water and vegetation during the summer. The research methods are mostly questionnaire surveys and simulations, whereas the urban form and climate characteristics of cold regions are rarely considered, and the results of these studies are seldom applied to design practices. This study focused on the thermal comfort of waterfronts in cold regions. Computational Fluid Dynamics, Mean Radiant Temperature, and the Universal Thermal Climatic Index were used to examine the effects of tree–water features (winter river ice and tree defoliation) on thermal comfort in cold regions, and correlation analyses were combined to screen for relevant urban morphology factors for waterfront thermal comfort. Regression analyses were conducted to understand the influence of six factors, namely, building orientation, floor area ratio, open space ratio, building height to distance between river and building ratio, standard deviation of the first building row, and vegetation concentration, on waterfront thermal comfort. After training and comparing the four thermal comfort prediction models, a genetic algorithm combined with an artificial neural network was applied to optimize the design of the urban morphology of the six waterfront blocks. The optimization improved the waterfront thermal comfort performance by 22%, 7%, 77%, 106%, 3%, and 3%, respectively.

Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.

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.

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.

Impact of lighting environment on human performance and prediction modeling of personal visual comfort in enclosed cabins

Enclosed cabins are of great significance in various fields, including national defense, scientific research, and industrial applications. It is important to clarify the impact of the lighting environment in these cabins on the people operating within them. This study investigated the effects of the lighting environment in enclosed cabins on the physiological, operational, and comfort performance of operators through simulated experiments. In Addition, using the Random Forest Algorithm and ExpandNet technique, we developed a prediction model to evaluate the comfort level of the lighting environment for personnel in enclosed cabins. The results indicated that pupil diameter exhibited the highest sensitivity to ambient light. The appropriate luminance combination of the screen and the ambient scene have a positive effect on human performance. In particular, it was observed that the average cognitive performance and comfort of participants tended to be relatively high in the luminance combinations 13, 14, and 15 at CCT 5500 K. The screen luminance of these combinations are all 284.75 cd/m2. Although no statistically significant relationship was found between the cognitive performance of the participants and their comfort, the comfort of the participants tended to decrease after the cognitive operations was completed. According to the proposed personal comfort prediction model, the visual comfort of different people varies even under the same lighting conditions. This study provides a solid theoretical basis for improving the design of lighting environments in enclosed spaces and contributes to developing a pleasant and productive working environment within limited cabins.

Review on Gaps and Challenges in Prediction Outdoor Thermal Comfort Indices: Leveraging Industry 4.0 and ‘Knowledge Translation’

The current outdoor thermal comfort index assessment is either based on thermal sensation votes collected through field surveys/questionnaires or using equations fundamentally backed by thermodynamics, such as the widely used UTCI and PET indices. The predictive ability of all methods suffers from discrepancies as multi-sensory attributes, cultural, emotional, and psychological cognition factors are ignored. These factors are proven to influence the thermal sensation and duration people spend outdoors, and are equally prominent factors as air temperature, solar radiation, and relative humidity. The studies that adopted machine learning models, such as Artificial Neural Networks (ANNs), concentrated on improving the predictive capability of PET, thereby making the field of Artificial Intelligence (AI) domain underexplored. Furthermore, universally adopted outdoor thermal comfort indices under-predict a neutral thermal range, for a reason that is linked to the fact that all indices were validated on European/American subjects living in temperate, cold regions. The review highlighted gaps and challenges in outdoor thermal comfort prediction accuracy by comparing traditional methods and Industry 4.0. Additionally, a further recommendation to improve prediction accuracy by exploiting Industry 4.0 (machine learning, artificial reality, brain–computer interface, geo-spatial digital twin) is examined through Knowledge Translation.

Influence of psychological and personal factors on predicting individual’s thermal comfort in an office building using linear estimation and machine learning model

ABSTRACT Thermal comfort in the building affects occupants’ health, productivity, and electricity use. Predicting occupants’ thermal comfort in advance will be helpful. Nowadays, a widely used model for thermal comfort prediction is the predicted mean vote (PMV). However, studies have found discrepancies between PMV and thermal sensation votes. In this paper, a comparative study was made by developing thermal comfort prediction in office building models using linear estimation and machine learning (ML) algorithms. Fanger’s six parameters and nine other parameters are considered for linear estimation and ML input parameters. These parameters are divided into two groups: the psychology group with the parameter’s thermal preference, thermal acceptance, overall comfort, air movement vote, humidity preference, and humidity vote, and the personal group with the parameters of gender and age. The predictive model developed showed the validated coefficient of correlation of more than 0.88 for both categories. For ML, training and evaluation have been done for five ML models: Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN). The findings showed that new parameters made significantly better thermal comfort prediction. RF has the lowest prediction average error, 0.706 when including all the psychological group parameters.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption. ML algorithms, however, require a large amount of data to be adequate. There may be privacy violations due to collecting this data. To tackle this problem, this study proposes a federated deep learning (FDL) architecture developed around a deep neural network (DNN) paradigm. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the general public. The data is normalized using the min-max normalization approach, and the Synthetic Minority Over-sampling Technique (SMOTE) is used to enhance the minority class's interpretation. The DNN model is trained separately on the dataset after obtaining modifications from two clients. Each client assesses the data greatly to reduce the over-fitting impact. The test result demonstrates the efficiency of the proposed FDL by reaching 82.40% accuracy while securing the data.

Comfort Prediction Method for Wearable Devices: Current Progress and Future Direction

Falls constitute a significant health risk, particularly among the elderly, thus prompting the introduction of various wearable devices capable of fall detection. However, the majority of these devices prioritize accuracy over wearer comfort, which significantly influences user adherence and, by extension, the broader development of wearable technologies. Addressing this oversight, this review first summarizes the current methods for predicting the comfort of wearable devices, evaluating them in terms of feasibility and accuracy, reliability and effectiveness, as well as safety and privacy. Subsequently, building upon the evaluation of existing methods, this review proposes a predictive solution based on the XGBoost algorithm.

Machine learning-based prediction of outdoor thermal comfort: Combining Bayesian optimization and the SHAP model

Rising global temperatures have resulted in urban heat waves in recent years, endangering residents' health and even their lives. As a result, accurate outdoor thermal comfort prediction is critical. The goal of this research is to develop highly accurate and interpretable machine learning models of outdoor thermal comfort. We created a summer outdoor thermal comfort dataset for cold regions using microclimate parameter measurements and questionnaires, and divided it into two datasets: without and with shading. The prediction performance of nine machine learning models was compared, as well as their prediction performance following Bayesian optimization. Finally, the SHAP model was used to explain the important features of the best machine learning models and their impact on thermal comfort. The results show that after partitioning the dataset by shading, the accuracy of the best machine learning prediction models for Thermal sensation vote (TSV), Thermal acceptable vote (TA), and Thermal comfort vote (TCV) in the unshaded space improves by 9.2%, 9.31%, and 6.16%, respectively, while the prediction accuracy in the shaded space remains basically unchanged. Extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and categorical boosting (CatBoost) outperform other methods. After Bayesian optimization (BO), the machine learning models' TSV, TA, and TCV prediction accuracies improved by 6.83%, 4.05%, and 2.55%, respectively. CatBoost model with Bayesian optimization (CatBoost + BO) was the best model in most cases. TSV, TA, and TCV predictions were heavily influenced by microclimate parameters, with mean radiant temperature being the most important in unshaded spaces and air temperature being critical in shaded spaces. Furthermore, age, body mass index, and emotional state all had a significant impact on TA and TCV. This research will contribute to improving the quality of life of urban residents by providing a scientific foundation for the design and planning of urban open space.

Improved grey principal component analysis neural network based adaptive thermal comfort model: Application in the enclosed cabin with microclimatic conditions

Predicting the thermal comfort of operators in enclosed cabins under extreme operational conditions is crucial for the enhanced and optimal design of cabin air circulation systems. In this study, an improved supervised machine learning algorithm, namely a Grey Principal Component Analysis (G-PCA) was proposed to evaluate the operators’ thermal comfort. The comprehensive dataset was first attained and constructed from the proposed 32 indicators, which recorded each tested object’s EEG and physiological features, core body temperature, skin temperature, and subjective assessments. The accuracy of the proposed model was demonstrated by comparing it with the results predicted via existing indicator sets (PMV and 7-point skin temperature) and modelling algorithms (BP and PCA BP). The results showed that the prediction accuracy of the PMV model, 7-point skin temperature, the 32 indicators were found to be 79.4%, 82.8%, 96.1%, utilising the G-PCA WNN algorithm, and the prediction accuracy of BP, PCA BP, G-PCA WNN is 82.3%, 89.9%, 96.1%, based on the 32 indicators. A conclusion could be drawn that the proposed 32 indicators can incorporate a more comprehensive range of thermal comfort information, and the improved G-PCA WNN thermal comfort prediction model achieved the best prediction performance among the compared algorithms and models.

Experimental study on the physiological parameters of occupants under different temperatures and prediction of their thermal comfort using machine learning algorithms

Real-time prediction of indoor thermal comfort has great potential in the development of a thermal comfort-driven control method for heating, ventilation, and air conditioning (HVAC) systems and the improvement of the indoor environment. This study measured physiological parameters (including electrocardiogram (ECG) signal, electroencephalogram (EEG) signal, and skin temperature (ST)) of 10 male and 10 female volunteers under three temperature conditions (22 °C, 25 °C, and 28 °C). A setup database containing 3600 sets of data was used to develop the thermal comfort prediction model with three machine learning algorithms: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN). Significant differences were found in both the values and the impact indices of physiological indicators between males and females (p < 0.05). The RF algorithm had a significant advantage with an overall accuracy of 89.3 %. If only one factor is employed for machine learning training, using the RF algorithm and training with the ST dataset is the most appropriate method, with an accuracy of 81.8 %. The addition of all datasets can increase the modeling accuracy to 95.6 %. Taking into account the sample imbalance, the SMOTETomek method was applied. After applying the SMOTETomek method to balance the data, the model accuracy reached at 97.3 %. The comprehensive results showed that thermal comfort prediction for the mixed group of males and females by the RF algorithm using one hybrid model was feasible. The establishment of this model laid the foundation for thermal comfort-driven HVAC control methods and the improvement of indoor environments.

Design of three outdoor combined thermal comfort prediction models based on urban and environmental parameters

The current trend of urbanization growth, take along a substantial increase in the outdoor temperatures within built environments, affecting the liveability of outdoor space, reducing the thermal comfort of citizens. Outdoor thermal comfort is usually evaluated with complex models on large scale, considering neighbourhoods, districts, or cities, without focusing on local and accurate predictions. The present study is aimed at contributing to the topic by proposing a simple yet accurate combined prediction model, able to assess the outdoor thermal comfort around a theoretical isolated building. A set of parameters, such as wind velocity, relative humidity, solar irradiation, and building height, has been used to quantify their effect urban comfort at fixed air temperature. The same parameters have been used for the development of a predictive correlation for the three outdoor thermal comfort indexes (UTCI, PET and SET). Comfort data was calculated by CFD simulations where the environmental parameters was variated in a fixed range. The main contribution to outdoor thermal comfort is provided by solar irradiation affecting the comfort indexes values up to 50%, the second is the wind velocity affecting up to 25% the thermal comfort indexes. The variation of building height is less impacting (around 15% of comfort values) as well as the relative humidity (5%). At least, three correlations have been elaborated for each outdoor thermal index. Correlation’s quality has been evaluated using RMSE and percentage difference, obtaining a maximum average value of 0.71 and 1.48%, respectively.

Data efficient indoor thermal comfort prediction using instance based transfer learning method

The precise prediction of indoor thermal sensation is critical to building energy efficiency and satisfaction of occupants. In this field, data-driven models have shown superior performance than Predicted Mean Vote (PMV) model. However, a reliable and accurate model of this kind relies on sufficient and high-quality data from physiological and environmental sensing, which in practice is difficult or expensive to collect. Rather than developing a separated predictive model for certain task, transfer learning method could intelligently use knowledge from similar domains to solve the target problem, and thus the requirement of data resources is reduced. In the last few years, transfer learning has been introduced into the field of thermal comfort prediction. Most of them used the source domain data as a whole for model pre-training, and the samples' suitability judgment was not fully considered. But the general situation in this field is: there are few public and available data resources and their data distributions are hardly consistent with that of specific target task. Aiming at the issue, this study proposes an instance based transfer learning model, which is based on data weighting and reusing for data efficient thermal comfort prediction. We assume that samples from source and target domains obey different distributions but have some common features that can be transferred across domains. The Nearest Neighbor Search (NNS) is used for similarity judgement and selection of source domain data; A Boosting type transfer learning algorithm, i.e., TrAdaBoost, is improved for reliable thermal comfort prediction. In the algorithm, an automatic weighting mechanism could optimally adjust the source training data's weights for performance improvement. By using field experimental data set and a public data set as target and source domains data respectively, the performance of the proposed model is investigated. Results show when target domain data is insufficient, the proposed NNS-iTrAdaBoost method has superior transfer learning ability and could achieve much better performance than traditional data-driven models. In practice, the proposed method is easy for engineering implementation and has great potential for thermal comfort prediction based on small-scale experiments.

Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics

Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an F1-score greater than 80% and can be combined with any wind rose statistic.

Prediction of the acoustic comfort of a dwelling based on automatic sound event detection

Abstract There is an increasing concern about noise pollution around the world. As a first step to tackling the problem of deteriorated urban soundscapes, this article aims to develop a tool that automatically evaluates the soundscape quality of dwellings based on the acoustic events obtained from short videos recorded on-site. A sound event classifier based on a convolutional neural network has been used to detect the sounds present in those videos. Once the events are detected, our distinctive approach proceeds in two steps. First, the detected acoustic events are employed as inputs in a binary assessment system, utilizing logistic regression to predict whether the user’s perception of the soundscape (and, therefore, the soundscape quality estimator) is categorized as “comfortable” or “uncomfortable”. Additionally, an Acoustic Comfort Index (ACI) on a scale of 1–5 is estimated, facilitated by a linear regression model. The system achieves an accuracy value over 80% in predicting the subjective opinion of citizens based only on the automatic sound event detected on their balconies. The ultimate goal is to be able to predict an ACI on new locations using solely a 30-s video as an input. The potential of the tool might offer data-driven insights to map the annoyance or the pleasantness of the acoustic environment for people, and gives the possibility to support the administration to mitigate noise pollution and enhance urban living conditions, contributing to improved well-being and community engagement.

Prediction of compressive comfort of graded compression sleeves based on calf feature classification

This study explored the relationship between the design of calf compression sleeves and the comfort of young women in long sitting and standing work environments. By studying the relationship between material elongation and comfort pressure, a prediction model was obtained. The characteristics of the calf models of 94 women were classified, and the influence of different calf characteristics on the pressure distribution and tensile value design was objectively analyzed through simulation tests. The samples were then produced based on the simulation results, and subjective and objective try-on tests and evaluations were carried out. The results show that the difference in the shape of the calf has a certain impact on the pressure distribution and comfort. The predicted value of the theoretical model is in good agreement with the actual test value. The sample can bring a suitable gradient pressure and meet the comfort requirements and safety standards.

A data-driven framework for thermal comfort assessment method based on user interaction

The rapid development of smart home systems requires a transition of thermal comfort studies. A feasible, user-friendly, consumer-oriented thermal comfort assessment method for large-scale residential applications is extremely urgent. However, most studies of thermal comfort models have focused on large commercial buildings. The application and deployment of such methods often rely on multiple prerequisites like relatively steady thermal conditions, regular energy consumption schedule, and additional sensor information. While situations are different in residential scenarios. In this paper, over 100 thousand air conditioner users were analyzed and a fundamental but overlooked question in thermal comfort research was established. In the household environment, the thermal condition is more complicated. Difference in age, gender, and usage preference leads to diversity in individual thermal comfort. Furthermore, introducing more sensors for better comfort prediction performance is not acceptable both from the cost perspective and the user perspective. To bridge the above missing gap, a novel thermal comfort assessment framework combining user interaction and metric learning was constructed. The proposed framework can be used to construct thermal comfort assessment systems by exploiting user interaction actions, which is a low-cost alternative and complementary to the traditional methods based on thermal equilibrium. The ease of construction makes the framework easy to integrate into smart home systems, addressing the difficulty of applying thermal comfort studies in residential scenarios.