Real Time Object Detection for Sustainable Air Conditioner Energy Management System

Air conditioning has become indispensable for maintaining human comfort, especially during hot weather, as people rely on it to stay cool indoors. However, the long-term and uncontrolled use of air conditioners has significantly contributed to climate change and environmental degradation. The extensive use of air conditioners releases more carbon dioxide, a greenhouse gas, into the atmosphere, exacerbating global warming and leading to adverse climate impacts. The proposed sustainable air conditioning energy management system aims to address this issue by optimising air conditioner use while minimising its environmental footprint and mitigating climate change. Current air conditioning systems in offices, buildings, and homes typically rely on fixed temperature settings, leading to excessive energy consumption and increased greenhouse gas emissions. Existing solutions, such as fixed timers, manual timer settings, and physical controllers, are ineffective as they cannot dynamically respond to changes in environmental conditions, such as room occupancy and activity levels, resulting in significant inefficiencies and environmental hazards. To overcome these limitations, the proposed system introduces an innovative solution using software engineering technology, specifically real-time object detection, to control air conditioning energy usage. This approach redefines air conditioning management by allowing the system to dynamically adapt to room occupancy, environmental factors, and activity levels, ensuring the right amount of cooling is delivered at the right time. This method represents a concrete and effective response to climate change challenges and demonstrates a commitment to creating a sustainable and environmentally responsible future.

@article{Ying2024,
  title     = {Real Time Object Detection for Sustainable Air Conditioner Energy Management System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Chang Shi Ying and C. PuiLin},
  doi       = {10.14569/IJACSA.2024.0151015},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151015}
}