No abstract

No abstract

To enable heating, ventilation and air-conditioning systems to effectively work for the next generation-built environment by reducing unnecessary energy loads while also maintaining satisfactory thermal comfort conditions, this present work introduces a demand-driven deep learning-based framework, which can be integrated with building energy management systems and provide accurate predictions of occupancy activities. The developed framework utilises a deep learning algorithm and an artificial intelligence-powered camera. Tests are performed with new data fed into the framework which enables predictions of typical activities in buildings; walking, standing sitting and napping. Building energy simulation was used with various occupancy profile schedules: two typical static office occupancy profiles, a schedule generated via the deep learning framework and an actual prediction profile. An office space within a case study building was modelled. Initial results showed that the overall occupancy heat gains were up to 30.56% lower when the deep learning generated profile was used; as compared to the static office occupancy profile. This indicated a 0.015 kW decrease in occupancy gains, which also influenced the increase in building heating loads. Analysis indicates the occupancy detection-based framework is a potential solution for the development of effective heating, ventilation and air-conditioning systems. Additionally, the requirement for the deep learning framework to work for multiple occupancy activity detection and recognition was identified.