This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.
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