Real-Time estimation of internal and solar heat gains in buildings using deep learning

This paper presents real-time monitoring of dynamic internal and solar heat gains using programmable low-cost cameras and deep learning techniques. For monitoring changes in occupancy, equipment, lighting, and window status in real time, a convolutional neural network (CNN)-based multi-head classification model was developed and trained with High Dynamic Range (HDR) images, collected using a low-cost fisheye camera in offices. A Python-based region of interest (ROI) generation program was developed to predefine the target areas for monitoring. The classification heads of the model were trained from scratch using a small office image dataset first; then, they were fine-tuned using another HDR image dataset collected in a larger open-plan office with more complex scenes, to evaluate the transferability of the model. The weighted mean classification precision and recall results showed that the developed model could classify the detailed status of each target heat gain (occupants, equipment, lighting, windows) in the predefined areas of the scene with great performance. Finally, to evaluate the impact of real-time monitoring of heat gains on energy demand, the open plan office space used for the experimental dataset collection was modeled using EnergyPlus software using (i) commonly assumed fixed schedules for occupancy, equipment and lighting and (ii) real-time monitored dynamic schedules for internal and solar gain components under the same weather conditions. The results showed that recommended fixed schedules may lead to significant errors in estimated internal and solar gains. The largest discrepancy was noted for occupancy and equipment usage, but other categories also showed both underestimation and overestimation of thermal load components. Implementing reliable and continuous monitoring of dynamic internal and solar heat gains is important for efficient demand-driven HVAC control.