Real-time detection method of window opening behavior using deep learning-based image recognition in severe cold regions

Abstract

The building industry plays an important role in the effort to achieve carbon neutrality, and window opening behavior is a major contributor to building energy consumption. In severe cold regions, opening windows during the heating period causes energy waste. If the window opening behavior can be monitored in real time, then the occupants' energy use behavior can be guided to reduce energy waste. This paper proposes a window opening behavior monitoring method using a deep learning-based image recognition model that can identify the window state based on real-time video and was validated in a case study based on outdoor thermal and wind data. A teaching building with casement windows was used as a case study. The results showed that the occupants preferred a large window opening angle (45°∼180°), which is a choice that significantly affects the building energy consumption. The prediction accuracy of the proposed method can reach 97.79%, which also serves to avoid the difficulty of manually calibrating the pixel threshold and realize real-time feedback. This method has a wide range of application scenarios that enable us to obtain window opening behavior to improve the building performance simulation accuracy and guide occupants' energy consumption behavior.