Enhancing the detection performance of a vision-based occupancy detector for buildings

Abstract

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F 1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing.