A coupled deep learning-based internal heat gains detection and prediction method for energy-efficient office building operation

Occupants' behaviour and the use of electrical equipment can significantly impact the building energy demand. Accurate occupancy and equipment usage information are key to improving the performance of demand-driven control, which can automatically adjust the heating, cooling and ventilation system operation. Employing static schedules is commonly used for the operation of heating, ventilation and air-conditioning systems, while it cannot satisfy the actual requirements due to the dynamic variations within the conditioned spaces. This study introduces a coupled real-time occupancy and equipment usage detection and recognition approach using deep learning and computer vision techniques for efficient building energy controls. The experimental results presented an overall equipment detection and occupancy activity detection accuracy of 78.39% and 93.60%. To investigate the influence of the implementation of the approach on building energy demand, a case study office building was selected to conduct experimental tests and modeled using a building energy simulation tool. Four scenarios with different occupancy and equipment profiles were defined and evaluated. The simulation results showed that heat gains, when employing static profiles were larger than the heat gains predicted when using the deep learning influenced profiles. Up to 53.95% lower heat gains were estimated when using both occupancy and equipment detection approaches than static schedules solely. The results highlighted the importance of monitoring real-time occupancy and electrical equipment usage and the advantages of using deep learning detection techniques to provide data for demand-driven controls, optimising building energy efficiency while maintaining a comfortable indoor environment.

Automated summary

Occupants' behavior and electrical equipment usage have a significant impact on building energy demand. This study proposes a real-time detection and recognition approach using deep learning and computer vision techniques to efficiently control building energy. Experimental results demonstrate high accuracy in equipment and occupancy detection, and a case study shows the influence of the approach on building energy demand. The results highlight the importance of monitoring real-time occupancy and electrical equipment usage and the advantages of using deep learning detection techniques to optimize building energy efficiency.