Abstract
Heating, ventilation and air-conditioning (HVAC) systems play a key role in shaping office environments. However, open-plan office buildings nowadays are also faced with problems like unnecessary energy waste and an unsatisfactory shared indoor thermal environment. Therefore, it is significant to develop a new paradigm of an HVAC system framework so that everyone could work under their preferred thermal environment and the system can achieve higher energy efficiency such as task ambient conditioning system (TAC). However, current task conditioning systems are not responsive to personal thermal comfort dynamically. Hence, this research aims to develop a dynamic task conditioning system featuring personal thermal comfort models with machine learning and the wireless non-intrusive sensing system. In order to evaluate the proposed task conditioning system performance, a field study was conducted in a shared office space in Shanghai from July to August. As a result, personal thermal comfort models with indoor air temperature, relative humidity and cheek (side face) skin temperature have better performances than baseline models with indoor air temperature only. Moreover, compared to personal thermal satisfaction predictions, 90% of subjects have better performances in thermal sensation predictions. Therefore, personal thermal comfort models could be further implemented into the task conditioning control of TAC systems.
Highlights
Heating, ventilation and air conditioning (HVAC) is a technology to create a suitable indoor environment, thermal environment and indoor air quality, for various types of buildings
Cameras, this study has proposed a cost-effective sensing system consisting of an infrared cameras, this study has proposed a cost-effective sensing system consisting of an infrared temperature array called AMG8833 as well as an air temperature and relative humidity temperature array called AMG8833 as well as an air temperature and relative humidity sensor called DHT22 to develop personal comfort models, including personal thermal sensor called DHT22 to develop personal comfort models, including personal thermal sensation and personal thermal satisfaction predictions with machine learning algorithms
This study aims to develop a dynamic task conditioning system controlled with personal comfort models with SVM featuring a non-intrusive sensing technique in a shared office room
Summary
Ventilation and air conditioning (HVAC) is a technology to create a suitable indoor environment, thermal environment and indoor air quality, for various types of buildings. Among various types conditioning systems in office buildings, the task ambient conditioning (TAC) system is one of the most energy efficient and comfortable space conditioning systems. The TAC system is defined as any space conditioning system that allows thermal conditions in small, localized zones to be individually controlled by building occupants, while still automatically maintaining acceptable environmental conditions in the ambient space of the building [1]. Since the TAC system takes individual thermal preferences into account and maintains the overall acceptable thermal environment, it has become one of the most promising air-conditioning systems in open-plan office buildings. Due to rapid development of building automation system (BAS), many researchers have investigated advanced control strategies so as to operate an advanced HVAC system more effectively and energy efficient in the open-plan office buildings recently [2,3,4]
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