Inferring Personal Visual Preferences and Heat Gain Estimation in Buildings using HDRI and Deep Learning Techniques

In high-performance building design, it is important to account for the dynamic influence of daylight on humans, as its non-visual effects significantly contribute to the regulation of various physiological and psychological functions. Furthermore, effective and controlled use of daylighting can lower energy consumption for electric lighting, while also minimizing internal lighting gains, excessive solar heat gains, and cooling energy demand. However, there are challenges in choosing appropriate metrics for modeling individual visual preferences and integrating them into control strategies, especially in smart control systems for high-performance buildings that demand self-tuning and personalized functionalities. Therefore, this Thesis aims to develop reliable features and a learning framework that reflect the occupant's visual preferences and can be incorporated into optimal daylighting control strategies using a low-cost high dynamic range imaging (HDRI) camera and deep learning techniques.

First, this Thesis presents a new method for classifying daylighting preferences based on deep learning models trained with pixel-wise similarity features extracted from pairs of luminance maps. A new composite luminance similarity index was developed, which utilizes the pixel-wise information from the entire luminance distribution and considers both the direction and magnitude of relative luminance change, instead of instantaneous metrics used in previous studies. The generated luminance and contrast similarity maps were directly used for training convolutional neural network (CNN) models to classify the occupant’s visual preferences. The results proved the superiority of the luminance similarity index map as a preference indicator variable. In contrast, common static lighting parameters could not estimate daylight preferences even when used in powerful computational models; they neglected visual information located in various parts of the visual scene and could not consider the change in perceived luminance distribution.

Second, this Thesis presents a novel method for inferring the relative degree of personal visual preference from pairs of luminance maps using convolutional autoencoder (CAE) and relative ranking concepts. There are practical challenges to utilizing trained CNN-based visual preference classification models for inferring the most preferable visual condition. Therefore, two-stage training was proposed starting from developing a CAE-based feature extraction module to make the model updatable from unseen luminance map characteristics and implementing the trained feature extractor to the visual preference inference model. To select the most preferable luminance distribution among the observed visual environments, the relative ranking concept was implemented in the CAE-based visual preference inference model in addition to binary classification layers. Then, the L2 norm and Euclidean distance were applied to determine the appropriate adjustment directions by analyzing the degree of difference between the captured luminance distribution and the inferred individual preferred luminance distribution. This analysis focused on the condensed latent pixels representing the window and background areas in each luminance distribution.

Finally, this Thesis expands the scope of utilizing low-cost HDRI sensors and deep learning techniques by demonstrating real-time monitoring of dynamic internal and solar heat gains in office spaces that are required for demand-driven control. 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. Then, 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 the 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.