AI-Driven Optimization Techniques for Energy-Efficient Building Systems

This paper investigates artificial intelligence applications for energy optimization in building systems. With buildings consuming approximately 40% of global energy, AI-driven solutions offer significant potential for sustainability improvements. We examine how machine learning algorithms, neural networks, and predictive models can enhance HVAC operations, lighting controls, occupancy detection, and maintenance protocols to reduce energy consumption while maintaining occupant comfort. Our comprehensive literature review and case study analysis demonstrate that AI-enhanced building management systems achieve 15-30% energy savings compared to conventional approaches, particularly in buildings with variable occupancy and changing external conditions. Despite implementation challenges including data quality issues, legacy system integration, and specialized expertise requirements, most buildings realize return on investment within 2-4 years. We conclude with recommendations for industry standards, policy frameworks, adoption of AI-driven energy optimization in the built environment.