Investigating the performance of AI-driven smart building systems through advanced deep learning model analysis

The significance of this research rests in its potential to revolutionize building management systems, improve energy efficiency, boost occupant comfort, and encourage sustainable urban growth. These include concerns about personal information leakage, the complexity of building systems, and the requirement for powerful computers. To tackle these challenges, a Multi-faceted Smart Buildings using Deep Learning Framework (M-FSB-DLF) is proposed. It combines a comprehensive sensor network for data acquisition, advanced deep learning models like convolutional and recurrent neural networks, and reinforcement learning algorithms for adaptive control systems. The M-FSB-DLF has several potential uses, such as HVAC control, security systems, energy management, and occupancy optimization. By extensively analyzing simulations, this study shows that the proposed methodology improves building performance indicators such as energy efficiency, occupant comfort, resource utilization, security, and operational cost. Energy efficiency, system dependability, and user satisfaction are all shown to have markedly improved by a 20 % reduction in the simulations. In addition to laying the groundwork for future studies in this exciting area, this research highlights the revolutionary effect that deep learning will have on innovative building technology. More smart, efficient, environmentally friendly cityscapes are within reach due to the M-FSB-DLF, a groundbreaking development in smart building technology. The proposed method increases the security system ratio by 94.8 %, energy management system ratio by 96.2 %, occupancy optimization ratio by 74.3 %, energy efficiency ratio by 94.5 %, and sustainable urban growth ratio by 96.2 % compared to other existing methods.