Abstract
Controlling thermal comfort in the indoor environment demands research because it is fundamental to indicating occupants’ health, wellbeing, and performance in working productivity. A suitable thermal comfort must monitor and balance complex factors from heating, ventilation, air-conditioning systems (HVAC Systems) and outdoor and indoor environments based on advanced technology. It needs engineers and technicians to observe relevant factors on a physical site and to detect problems using their experience to fix them early and prevent them from worsening. However, it is a labor-intensive and time-consuming task, while experts are short on diagnosing and producing proactive plans and actions. This research addresses the limitations by proposing a new Internet of Things (IoT)-driven fault detection system for indoor thermal comfort. We focus on the well-known problem caused by an HVAC system that cannot transfer heat from the indoor to outdoor and needs engineers to diagnose such concerns. The IoT device is developed to observe perceptual information from the physical site as a system input. The prior knowledge from existing research and experts is encoded to help systems detect problems in the manner of human-like intelligence. Three standard categories of machine learning (ML) based on geometry, probability, and logical expression are applied to the system for learning HVAC system problems. The results report that the MLs could improve overall performance based on prior knowledge around 10% compared to perceptual information. Well-designed IoT devices with prior knowledge reduced false positives and false negatives in the predictive process that aids the system to reach satisfactory performance.
Highlights
A comfortable indoor environment is one of the most critical factors impacting humanlife quality
This study has addressed limitations in indoor thermal comfort that have helped engineers and technicians to monitor and detect problems on physical sites automatically
We proposed a new design and development of the Internet of Things-driven fault detection system to imitate human-like perception
Summary
A comfortable indoor environment is one of the most critical factors impacting humanlife quality (e.g., health, wellbeing, and working productivity performance). Indoor thermal comfort concerns engineering processes to control the environment for satisfying occupants in the building. Ventilation, and air-conditioning systems (HVAC systems) are engineering mechanisms required to handle ambient conditions of indoor thermal comfort to provide occupancy comfort level. HVAC systems play a crucial role in controlling indoor ambient conditions by transferring heat airflow from indoor to outdoor. HVAC systems, the outdoor environment, and indoor thermal comfort depend on each other to properly control such situations to be comfortable. The case needs engineers to monitor relevant factors from the indoor environment [1], outdoor environment [2], and HVAC system mechanism [3] and to prevent problems that can occur. Enormous systems may run continuously in real-world buildings, and fault detection and diagnosis of indoor thermal comfort based on these complex factors are beyond manual investigation by engineers and technicians. It is challenging to apply advanced technologies to detect and diagnose such relevant factors automatically
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
