Sustainable Smart Buildings with AI-Based Energy Management
TL;DR
Findings indicate that AI-based systems can significantly improve energy efficiency, reduce carbon emissions, and enhance comfort, though challenges such as data privacy, system complexity, and initial costs remain.
Abstract
Sustainable solutions in the built environment have become essential due to rapid urbanization and rising energy demands. Buildings account for nearly 40% of global energy consumption, making them a critical focus for energy efficiency and environmental sustainability. This paper explores AI-driven energy management systems in smart buildings, highlighting their ability to optimize energy use, reduce costs, and minimize environmental impact while maintaining occupant comfort. By integrating technologies such as IoT, machine learning, predictive analytics, and automation, these systems enable real-time monitoring and adaptive energy optimization. The study reviews traditional building management systems and identifies their limitations, proposing a layered architecture involving data acquisition, processing, prediction, and control. Machine learning techniques like ANN, SVM, and Reinforcement Learning are evaluated for energy forecasting and optimization. Findings indicate that AI-based systems can significantly improve energy efficiency, reduce carbon emissions, and enhance comfort, though challenges such as data privacy, system complexity, and initial costs remain. The research provides a practical framework for developing sustainable, energy-efficient smart buildings.