A novel vision-based approach for accurate equipment usage detection for energy efficiency and indoor comfort level improvement in office buildings - Wei, Shuangyu (2023)
The aim of this study was to develop a real-time indoor equipment usage detection approach using computer vision and deep learning techniques to aid the adjustment of heating, ventilation, and air conditioning (HVAC) operations based on the actual demands in office and kitchen spaces. This could not only address the issue of under- or over-estimation of the building energy consumption but also maintain a comfortable indoor environment for occupants by adjusting HVAC’s operations based on the intuitive and real-time data. The indoor equipment usage detection model was developed and implemented using Python and TensorFlow API. This work used AI-enabled cameras as the indoor detection technique and locally running trained deep learning algorithms to analyse and take action based on how equipment was utilised in the spaces. Experimental tests were carried out in the case study office and kitchen to assess the detection performance of the developed approach. The results indicate that the developed deep learning detection approach could achieve 82.52% accuracy in detecting office equipment and 91.42% in detecting kitchen equipment.