Citation: Sahoh, B.; Kliangkhlao, M.;
Kittiphattanabawon, N. Design and
Development of Internet of
Things-Driven Fault Detection of
Indoor Thermal Comfort: HVAC
System Problems Case Study. Sensors
2022, 22, 1925. https://doi.org/
10.3390/s22051925
Academic Editors: Kim Phuc Tran,
Athanasios Rakitzis, Khanh T.
P. Nguyen and Giovanni Betta
Received: 12 February 2022
Accepted: 27 February 2022
Published: 1 March 2022
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Article
Design and Development of Internet of Things-Driven Fault
Detection of Indoor Thermal Comfort: HVAC System Problems
Case Study
Bukhoree Sahoh
1,2,
* , Mallika Kliangkhlao
3
and Nichnan Kittiphattanabawon
1
1
School of Informatics, Walailak University, Tha Sala, Nakhon Si Thammarat 80160, Thailand;
knichcha@wu.ac.th
2
Informatics Innovation Center of Excellence (IICE), Walailak University, Tha Sala,
Nakhon Si Thammarat 80160, Thailand
3
Department of Computer Engineering, Prince of Songkla University, Had Yai 90112, Thailand;
kliangkhlao.m@gmail.com
* Correspondence: bukhoree.sa@wu.ac.th
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.
Keywords:
machine learning; consciousness prior; heat transfer; indirect measurement; air-handling
unit; building sustainability; internet of things
1. Introduction
A comfortable indoor environment is one of the most critical factors impacting human-
life 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. Heating, ventilation, and air-conditioning systems (HVAC systems) are
engineering mechanisms required to handle ambient conditions of indoor thermal comfort
to provide occupancy comfort level. Significantly, in the tropical zones (e.g., parts of
North America, South America, Africa, Asia, and Australia), outdoor environments are
warm throughout the year and influence the indoor environment to become uncomfortable.
HVAC systems play a crucial role in controlling indoor ambient conditions by transferring
heat airflow from indoor to outdoor. Therefore, HVAC systems, the outdoor environment,
and indoor thermal comfort depend on each other to properly control such situations
Sensors 2022, 22, 1925. https://doi.org/10.3390/s22051925 https://www.mdpi.com/journal/sensors
