Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Fay, Damien; O'Toole, Liam; Brown, Kenneth N.

doi:10.1016/j.pmcj.2016.08.012

Cited by 13 publications

(10 citation statements)

References 30 publications

(65 reference statements)

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“…However, collected thermal perception data usually contains considerate artefact and noise. To solve this problem, the study presented in [67] explored using Gaussian Process (GP) Regression to extract human subjects' thermal preferences from the collected data contaminated with measurement noise. Their analysis showed that the GP method effectively rejected outliers/deadband and achieved accurate prediction of human subjects' thermal preference.…”

Section: ) Regression Algorithmmentioning

confidence: 99%

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Song

Mao

Liu³

2019

IEEE Access

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Occupants' comfort perception about the indoor environment is closely linked to their health, wellbeing and productivity. Improvement of comfort level in office buildings has significant positive impacts on both employers and employees. Human comfort in indoor environment usually can be assessed in four aspects: thermal comfort, visual comfort, acoustic comfort and respiratory comfort. In this paper, we present a literature review on the previous research contributions towards studying various aspects of human comfort with a special focus on the respective assessment criteria, data collection methods and data analysis approaches employed by former studies. Previous review work has covered the fundamental concepts associated with human comfort. However, their studies mainly focus on thermal comfort and there is limited work that covers other aspects of comfort. Moreover, few of them discuss how the data is obtained, how to extract useful information from the data and how the data is analyzed. To fill up this gap, this paper conducts the survey from the data-driven point of view. Through the survey, we find that sensor technology has been widely used in the data collection for various types of comfort, while so far the machine learning approaches are mainly applied in the area of thermal comfort study. Finally, some potential future research areas are proposed based on the current status of the research work. The established knowledge in this paper would provide useful insights for engineers or researchers who embark on their research in this area. INDEX TERMS Human comfort, thermal comfort, assessment criteria, data analysis method, sensor technology, machine learning.

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Section: ) Regression Algorithmmentioning

confidence: 99%

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Song

Mao

Liu³

2019

IEEE Access

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“…As we mentioned above, our work focuses on the regression model since we aim to continuously monitor the comfort levels of individuals. Thus, we primarily review the regression models [18]- [24] and other continuous prediction models [14]- [17], [28]- [30].…”

Section: Related Workmentioning

confidence: 99%

“…The study presented in [23] developed a thermal comfort model with a kernel based method which was used to learn occupants' thermal comfort profile. Gaussian Process (GP) regression was proposed in [24] to extract subjects' thermal preferences. However, these models mainly concentrate on thermal comfort and involve human thermal perception votes which is subjective in nature.…”

Section: Related Workmentioning

confidence: 99%

“…Basically, previous studies mainly carry out research on thermal comfort models. These models are generally classified into three categories: Regression [18]- [24], Classification [25]- [27] and other models [14]- [17], [28]- [30]. Regression models primarily use the PMV index as the subjective comfort index to predict continuous comfort levels.…”

Section: Introductionmentioning

confidence: 99%

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Environmental and Human Data-Driven Model Based on Machine Learning for Prediction of Human Comfort

2019

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Occupants' comfort level has a strong correlation with health problems. Providing a comfortable environment for the occupants will bring the benefits of improved health. To achieve this goal, it is necessary to have a reliable human comfort model for predicting the occupants' comfort level and subsequently controlling the involved comfort condition. However, the comfort perception of occupants is subjective. There is a lack of objective indices for measuring comfort level. Furthermore, human comfort is affected by various environmental factors. Such situations make it difficult to set up a model for measuring human comfort. To address the challenges, we use Blood Pulse Wave (BPW) as an objective comfort index and adopt a data-driven approach to predict human comfort level based on data including both environmental factors and human factors. We propose a framework for collecting the data followed by investigating the relationship between the factors with the purpose of building a scalable comfort model. In consideration of the nonlinear relationship present in the dataset, we opt for support vector regression with radial basis function (SVR-RBF) algorithm to establish the comfort model. To validate the predication performance of this method, we have applied the other six popular machine learning models on the same dataset. In order to choose an optimal model, we apply the holdout method and k-folder cross-validation method together with the grid search. The comparison results show that the SVR-RBF has the best performance for comfort prediction according to the mean squared error, mean absolute error and R-squared score.INDEX TERMS Machine learning, human comfort prediction, support vector regression.

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“…However, the data used is often limited by administrative overhead, cohort size, and users' behaviour during the experiments (i.e. survey fatigue), producing a class-balance issue that is often ignored or remove [1]. In this paper, we proposed the use of Generative Adversarial Networks (GANs) as a pre-processing step to tackle the class-imbalance nature of subjective human responses datasets.…”

Section: Introductionmentioning

confidence: 99%

Towards Class-Balancing Human Comfort Datasets with GANs

Quintana

Miller

2019

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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Human comfort datasets are widely used in smart buildings. From thermal comfort prediction to personalized indoor environments, labelled subjective responses from participants in an experiment are required to feed different machine learning models. However, many of these datasets are small in samples per participants, number of participants, or suffer from a class-imbalance of its subjective responses. In this work we explore the use of Generative Adversarial Networks to generate synthetic samples to be used in combination with real ones for data-driven applications in the built environment. CCS CONCEPTS • Computing methodologies → Machine learning; Modeling and simulation.

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Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Cited by 13 publications

References 30 publications

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Environmental and Human Data-Driven Model Based on Machine Learning for Prediction of Human Comfort

Towards Class-Balancing Human Comfort Datasets with GANs

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