Optimized Power Consumption for Intelligent Architecture with AI/ML and IoT Integration

Abstract

The overall attempt addresses the merging of Artificial Intelligence (AI), Machine Learning (ML) and Internet of Things (IoT) technologies to benefit power usage in intelligent buildings. Traditional methods for energy management are often limited in their power to adjust to dynamic conditions, which brings about wastefulness. In this study, a system for the Internet collects data in real-time from several sensors, such as temperature, occupancy, and energy usage. ML algorithms are deployed to this data for predictions and to optimize utilization of electricity trends. The technology automatically changes lighting, HVAC, and other building functions to lower energy use without affecting tenant comfort. A simulation-based test bed is created of assessing the system's performance. Results demonstrate a large reduction in power usage compared to conventional procedures, leading to higher energy efficiency and cost savings. The study underlines the potential of AI, ML and IoT to alter smart decisions about the structure and contribute to sustainable energy practices.

Keywords

• intelligent buildings
• machine learning
• internet of things (iot)
• energy optimization
• power consumption management