DeepVision based detection for energy-efficiency and indoor air quality enhancement in highly polluted spaces

Cooking can generate substantial heat from cooking equipment, potentially resulting in low thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can profoundly affect indoor air quality (IAQ) in not only kitchen spaces but also adjacent spaces without sufficient ventilation. To ensure a healthy and comfortable environment and avoid unnecessary energy use, this research proposed an equipment usage detection approach utilizing computer vision and deep learning techniques to aid the operation of demand-driven ventilation systems in highly polluted spaces. Faster RCNN model was employed and trained to perform kitchen equipment usage detection. The ventilation rate for kitchen spaces was adjusted based on the real-time detection results. Experimental tests were carried out in a case study kitchen and results showed that the detection model achieved an overall F1 score of 0.9142. Overall, the model achieved good performance in real-time detection, with high accuracy in identifying appliances in use such as stoves, ovens, and toasters. Field experimental results showed the advantages of combining mechanical ventilation methods, such as extractor fans and cooker hoods, to mitigate IAQ issues while achieving energy savings. Moreover, the energy simulations demonstrate its potential to reduce energy consumption by dynamically adjusting ventilation rates based on real-time equipment usage. When the fan speed varied with the real-time detection method, PM2.5 concentration in the cooking period was 16.5 % lower with only a 3.7 % rise in fan power and 0.8 % rise in daily heating demand, as compared to constant fan speed.