Abstract
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.
Highlights
The health and wellbeing of employees is of a great concern to business
In the context underlined above, the objective of this paper is to present a literature survey on the previous studies conducted on human comfort analysis in indoor environment with a main focus on the data collection methods and data analytics approaches that have been employed by different research groups
Evaluation of the effectiveness of the model for various scenarios should be carried out to decide whether the model is underfitting, overfitting or well generalized according to the performance of the trained model applied on the unseen data
Summary
The health and wellbeing of employees is of a great concern to business. According to statistics, about 90% of the overall business operating cost is spent on staff cost including medical benefit paid for employee [1]. Promoting health and wellbeing at work contributes to employees’ active engagement and improved productivity, and leads to remarkable savings in operating cost for employers [2]. For these reasons, one of the requirements for green office building is to provide an acceptable indoor environmental quality (IEQ) in view that it. Poor IEQ may lead to increased medical cost resulted from the building-related health issues and has adverse effects on employees’ work performance, whereas good IEQ demonstrates positive effect on the business in terms of improved recruitment, lower turnover rate and increased productivity [9].
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