AI-Driven Sustainable Energy Optimization in Smart Buildings
Abstract
The increasing energy consumption of buildings necessitates intelligent solutions for optimizing resource usage and reducing carbon footprints. This paper investigates machine learning-based energy management systems for smart buildings, focusing on occupancy detection, demand-side energy forecasting, and adaptive HVAC control. Reinforcement learning and deep neural networks are utilized to enhance energy efficiency, minimize waste, and lower operational costs. Case studies demonstrate how AI-driven smart building systems contribute to sustainable urban infrastructure by optimizing energy consumption and reducing environmental impact.
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