Convolutional Neural Network (CNN) for Building Energy Efficiency Analysis, Prediction, and Real-time Adjustment Strategies

In the era of sustainable development, building energy efficiency stands out as a pivotal concern, given the significant energy consumption attributed to buildings globally. This paper introduces a novel approach, harnessing the power of Convolutional Neural Networks (CNN) to analyze and predict energy inefficiencies using thermal imaging data. By training our model on a diverse dataset of thermal images from various buildings, we demonstrate the potential of CNNs in not only identifying energy inefficiencies but also suggesting real-time adjustment strategies. Preliminary results indicate a promising avenue for the integration of deep learning in the domain of sustainable architecture and building management.