A Study on AIoT-Based Indoor Air Quality Management for Comfortable Indoor Air Quality and Electrical Power Consumption Reduction

Recently, the Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT) through its combination with artificial intelligence (AI) technology and has become capable of providing intelligent services in all industrial sectors. Globally, energy consumption within buildings is continuously increasing alongside the advancement of IT and AI technologies. Since this increase is attributed to various causes—ranging from large-scale climate change to small-scale indoor environmental factors (air quality) and health factors—research aimed at reducing indoor energy consumption is actively underway. In particular, in the home environment where people spend a significant portion of their day, maintaining indoor air quality (IAQ) is critical for health, and energy conservation in heating, ventilation, and air conditioning (HVAC) systems is essential. In Korea, the number of single-person households is increasing and was expected to reach 36.1% of all households by 2024, leading people to live in increasingly smaller homes. This study aimed to verify residents using contactless facial recognition to prevent pandemics such as COVID-19 and to provide comfortable indoor air quality. Resident facial recognition was performed by identifying residents’ faces in images captured by the Pi camera using OpenCV’s Haar feature-based cascade classifier. Indoor air quality measurements were conducted in four indoor locations, measuring various environmental factors (PM2.5, CO2, etc.) based on environmental sensors and the IoT. Furthermore, to manage indoor air quality, AI was utilized based on the measurement data to classify the four spaces, with a success rate of 96%. Additionally, considering the indoor area of the experimental environment (97 m2), it was confirmed that operating a 70 W air purifier only when the resident is indoors can reduce power consumption by approximately 33–75% compared to running it 24 h a day.