藉由人與建築的互動發展個人化熱舒適模型與空調控制策略

黃立德

doi:10.6342/NTU201903692

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藉由人與建築的互動發展個人化熱舒適模型與空調控制策略

Development of personalized thermal comfort profiles and AC control strategies through human-building interactions

黃立德 , Masters　　Advisor：詹瀅潔　　

英文 DOI： 10.6342/NTU201903692

預測平均投票模型；個人化使用者熱舒適模型；使用者行為；在線學習；自動化控制； predicted mean vote (PMV) ； personalized occupant thermal comfort profile ； occupant behavior ； online learning ； automated control

Abstract │ Reference (44) │ Altmetrics

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Nowadays, we spend more than 90% of our time inside of buildings. People rely on more heating, ventilation, and air conditioning (HVAC) systems to maintaining indoor environmental comfort. In order to achieve comfort in the indoor environment, it also costs a huge amount of energy. However, when we maintain the daily operation of the building, we are still don't know how to fit the thermal comfort needs of the main body of the service: the occupants.
Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment. Researchers use many methods want to find indicators that can effectively quantify occupant behavior. One of the indexes have long-term development and also widely known is the predicted mean vote (PMV) index. Each occupant accepts the environmental comfort range is different. Further, when the occupant cannot tolerate thermal comfort and trigger the activity, which is called occupant behavior; and it includes the difference tolerance of each person. Past research has pointed out that occupants directly affect the efficiency of energy use, and therefore develop a method to capture, simulate or establish occupant behavior model is an important topic. 
In this study, we establish a relationship between PMV index and occupant behavior, and then import the Bayesian approach analysis, to get a personalized occupant thermal comfort profile (model). Then we according to the thermal comfort profile to develop a strategy to control the environment to reduce the interaction between the occupant and the building components (like air conditioner), as the first step of energy saving.
The study was conducted experiments through our own cloud management platform, which has environmental data monitoring and air conditioner (AC) control capabilities. The experiments were conducted to verify the effectiveness of the establishment of personalized thermal comfort profiles and AC control strategies.
From the experimental results, we can through the occupant interactions with the building components to create a personalized thermal comfort profile, by the online learning system and apply automated control to the environment according to the thermal comfort profile.

Reference ( 44 ) 〈TOP〉

1. Taiwan Green Productivity Foundation, ENERGY AUDIT ANNUAL REPORT FOR NON- PRODUCTIVE INDUSTRIES 2018. 2018.
2. Hong, T., et al., An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 2015. 92: p. 764-777.
3. Kwon, M., et al., Personal control and environmental user satisfaction in office buildings: Results of case studies in the Netherlands. Building and Environment, 2019. 149: p. 428-435.
4. American Society of Heating Refrigerating and Air-Conditioning Engineers and American National Standards Institute, Thermal environmental conditions for human occupancy. ANSI/ASHRAE standard,. 2013, Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers. 52 pages.
5. Ormandy, D. and V. Ezratty, Health and thermal comfort: From WHO guidance to housing strategies. Energy Policy, 2012. 49: p. 116-121.

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