A SYSTEMATIC LITERATURE REVIEW ON AI-ENABLED SMART BUILDING MANAGEMENT SYSTEMS FOR ENERGY EFFICIENCY AND SUSTAINABILITY

This systematic review has demonstrated that Artificial Intelligence (AI) plays a transformative role in Smart Building Management Systems (SBMS), enhancing energy efficiency, predictive maintenance, and sustainable automation. By analyzing 472 high-quality studies, this research has identified that AI-driven HVAC optimization, lighting control, solar energy forecasting, and demand-side energy management significantly reduce energy consumption, with reported efficiency improvements ranging between 20-50%. The review also highlights that reinforcement learning (RL) and deep learning (DL) models outperform traditional rule-based systems by dynamically adjusting building operations based on real-time sensor data, occupancy patterns, and environmental conditions. AI-powered fault detection and predictive maintenance further improve building operations by reducing unexpected system failures, lowering maintenance costs by up to 35%, and extending equipment lifespan. Moreover, the study underscores the growing potential of hybrid AI models integrating IoT, blockchain, and cloud computing in enabling real-time energy monitoring, decentralized energy trading, and secure automation. Despite these advancements, the review also reveals critical research gaps, particularly the lack of large-scale empirical validation, challenges in AI scalability, and the need for interdisciplinary collaboration to enhance AI’s effectiveness in sustainable building design. While theoretical and simulation-based studies provide strong evidence of AI’s benefits, real-world pilot projects, regulatory frameworks, and cross-sector collaborations are essential for AI-driven smart building technologies to achieve widespread adoption. Addressing these challenges through industry-academia partnerships, policy support, and further longitudinal research will be key to ensuring that AI-powered SBMS can drive long-term sustainability, operational efficiency, and energy resilience in modern smart infrastructure.