Comprehensive Energy Efficiency Analysis in Buildings Using Drone Thermal Imagery, Real-Time Indoor Monitoring, and Deep Learning Techniques

This paper presents a comprehensive approach to building energy efficiency analysis by integrating outdoor drone thermal imagery with real-time indoor heat envelope monitoring using deep learning techniques. The study builds upon our previous work that employed a YOLOv4 model to identify heat envelopes from drone-captured thermal images. To enhance the scope and robustness of the analysis, we introduce a complementary indoor monitoring system that utilizes a thermal camera for capturing real-time images of interior building spaces. Due to the scarcity of labeled indoor thermal data, we employ web scraping techniques to collect a diverse dataset for model training. The trained model is then evaluated on real-time indoor thermal images, demonstrating its effectiveness in identifying heat envelopes and potential energy inefficiencies. The combination of outdoor and indoor monitoring provides a holistic view of building energy performance, enabling targeted interventions for improved efficiency. This research advances the field of energy efficiency analysis by leveraging state-of-the-art deep learning algorithms and diverse data sources, offering a scalable and data-driven approach to building energy management. The proposed methodology has significant implications for reducing energy consumption, costs, and environmental impact in both residential and commercial buildings.