Indoor thermal comfort affects occupants’ daily activities and health. HVAC systems are necessary to control thermal comfort quality. Tracking and monitoring the effectiveness of HVAC system engines are critical activities because they ensure that the system can produce suitable indoor thermal comfort. However, the operation of such systems depends on practitioners and engineers, which is time-consuming and labor-intensive. Moreover, installing physical sensors into the system engine may keep track of the problem but may also require costs and maintenance. This research addressed this concern by presenting deep learning (DL)-driven automated fault detection and diagnostics (AFDD) for HVAC systems. It employed contextual factors as an indirect measurement to avoid modifying HVAC system engines (e.g., according to standard building appliance warranties) but was still able to effectively detect issues. The design and development of the DL model are proposed to encode complex behaviors of an HVAC system using contextual factors. The experimental results show that the predictive performance of our model achieved an average F-measure of over 97%, which was outstanding compared with the standard ML models. This proposed model will be a natural fit for AFDD for HVAC systems and is ready for future real-world applications as required by building engineering.

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.

Unexplainable indoor thermal comfort events from black-box models influence people to distrust suggestions from decision-support systems and ask for help from engineers and practitioners that are labor intensive and time consuming. These problems come from unknown cause and effect in the environments that cause the system not to produce explainable outcomes. This study proposes the cause-and-effect discovery for indoor thermal comfort events that help systems make human-like explanations to overcome these issues. The research contributions consist of three essential points. The first is perceptions based on the Internet of Things technologies that imitate human perception organs, which could sense signals as a system input component. The second is qualitative knowledge representation using random variable systems and graphs as the ground truth—the representation stores in the manner of human-like intelligence that people and systems can understand. The third is causal discovery algorithms that automatically determine the cause and effect in machine learning (ML) models from observational data. The results showed that models could discover cause-and-effect relationships close to the human-like intelligent-based model blueprint given observational data. They produce reasonable explanations for indoor thermal comfort events that help people trust such information and utilize it to make decisions.