การประยุกต์ใช้ AI เพื่อควบคุมระบบปรับอากาศตามปริมาณผู้ใช้งาน

Tarin Gurin; Chaiyaporn Khemapatapan

doi:10.65205/jcct.2026.e3915

Authors

Tarin Gurin College of Engineering and Technology, Dhurakij Pundit University, Bangkok 10210, Thailand
Chaiyaporn Khemapatapan College of Engineering and Technology, Dhurakij Pundit University, Bangkok 10210, Thailand https://orcid.org/0009-0008-8393-7478

DOI:

https://doi.org/10.65205/jcct.2026.e3915

Keywords:

Air Conditioning Control, YOLOv8n, Raspberry Pi, Object Detection, Person Detection, Energy Saving

Abstract

This research aimed to develop and evaluate a prototype of an occupancy-driven air conditioning control system. The system integrated human detection technology using the YOLOv8n algorithm with a Raspberry Pi processing unit an edge computing solution that is cost-effective, low-power, and compact—for real-time occupancy monitoring and counting for controlling air conditioning units via infrared signals. The system's performance was evaluated through 791 recorded events, focusing on detection accuracy under real-world indoor environmental lighting variations varying environmental conditions and control responsiveness. The results indicated high robustness to lighting variations, achieving an overall average accuracy of 92.21% with a Mean Absolute Error (MAE) of 0.44 people. The primary cause of detection error was attributed to person-to-person occlusion during high occupancy periods rather than lighting conditions. Furthermore, the system effectively adjusted air conditioning operations based on real-time occupancy, significantly reducing unnecessary energy consumption when spaces were unoccupied. These findings demonstrate the potential of the developed system for integration into smart buildings and sustainable energy management applications.

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