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|Title:||A novel real-time monitoring, notification, analytics system, and personal thermal sensations model for indoor air quality and energy efficiency in commercial buildings||Authors:||Li, Qing Yun||Degree:||Ph.D.||Issue Date:||2021||Abstract:||Indoor Air Quality (IAQ) covers the environmental conditions inside a building and its effects on occupants or residents (occupant health, wellness, comfort and productivity) are critical. In general, the quality of air can be assessed by measuring the various physical parameters, such as temperature, humidity, CO2, CO, TVOCs, PM10, PM2.5, etc. Firstly, with the concept of green economy, commercial area, especially the hotel industry, has started to consider a sustainable design and operation to obtain higher competitiveness. It is needed to implement a convenient way to recognize the environmental problems and find a real-time solution to monitor the Indoor Air Quality (e.g. temperature, relative humidity, CO2, TVOCs, PM2.5, etc.) and the relevant energy efficiency in commercial buildings. This study aims to develop a scalable integrated platform for providing real-time monitoring, alert notification, and analytics of IAQ so as to increase the level of the occupants' comfort and satisfaction. Facility managers could gain insights from the real-time hierarchical and historical information of IAQ and take actions at a point of time. This novel approach could help create a comfortable and healthy environment for the occupants in commercial buildings and balance the energy efficiency at the same time. Taking a hotel as a study case, the proposed platform was validated with three guestrooms in a hotel of Hong Kong, and the scalability of the system was fully confirmed. In the future studies, it is expected to deploy the proposed platform to a larger physical entity of the hotel, providing flexibility and expandability in accordance with the strategic facility planning of the hotel. Furthermore, it is planned to develop various kinds of occupant-centered services at a hotel guestroom level.
Secondly, this study utilized the latest mobile app technology-hybrid app to develop user-friendly periodic questionnaires to respond the Indoor Air Quality (IAQ) survey. This hybrid app technique is cost effective and easy to use, not only for users but also for facility managers. The hybrid app method makes the survey work simple and faster and such kind of semi-real-time feedback can be realized by periodic questionnaires. A case study was conducted to evaluate the feasibility of the developed hybrid app, focusing on the occupants' responses on the Indoor Air Quality. More than 100 respondents from the Open University of Hong Kong (OUHK) were invited to provide their feedbacks on their personal experiences about IAQ at a newly built building. The feedback was collected by a mobile app-based periodic questionnaire survey of personal feeling about IAQ at different time slots (at beginning, middle and end of the teaching lecture). This hybrid app-based survey shows its advantage for helping IAQ researchers to easily launch an efficient, safe and high-quality survey, comparing with the traditional paper-based, web-based and native app-based survey methods, in terms of development, maintenance, cost and speed, mobile device features (camera, the GPS, the accelerometer, etc.) and device's notification system in questionnaire survey. Furthermore, the hybrid app-based survey methodology can be easily used in other survey areas. Thirdly, this study developed a Random Forest classification algorithm based thermal sensation model, which combined IAQ parameters, personal information, physiological factors, and occupancy preferences on selection of 7-level of sensation: cold, cool, slightly cool, neutral, slightly warm, warm, and hot as input to form a corresponding thermal sensation model, to fulfill thermal comfort requirements of the occupants, considering the energy consumption. The personal thermal sensation model is used as the main component for personalized conditioning system (PCS), which is an effective method to fulfill thermal comfort requirements of the occupants, considering the energy consumption. Our model shows better functionality, as well as performance and factor selection. As a result, our method has achieved 70.2% accuracy, comparing with the 57.4% accuracy of support vector machine (SVM), and 67.7% accuracy of neutral network (NNet) in an ASHRAE RP-884 database. Therefore, our newly developed model can be accurately used in personalized thermal adjustment systems with intelligent control functions.
|Subjects:||Air quality management
Indoor air pollution
Buildings -- Energy conservation
Hong Kong Polytechnic University -- Dissertations
|Pages:||xvii, 138 pages : color illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/11153
Citations as of May 15, 2022
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