AI-Driven Solutions for Building Energy Efficiency - Singh, Riya; Agrahari, Sahil (Journal of Advanced Research in Applied Artificial Intelligence and Neural Network, 2025)
As the global population becomes increasingly urbanised, the demand for energy- efficient buildings has surged. Buildings now consume about 40% of total global energy, with HVAC (Heating, Ventilation, and Air Conditioning) systems accounting for a substantial share. Traditional energy management systems struggle to adapt to real-time demands and environmental fluctuations. In contrast, Artificial Intelligence (AI) offers a dynamic, adaptive, and predictive solution to these challenges. By leveraging machine learning (ML), deep learning (DL), reinforcement learning (RL), and data from IoT sensors, AI can anticipate energy needs, identify anomalies, and autonomously optimise energy consumption patterns. This paper provides a comprehensive analysis of AI mechanisms for enhancing building energy efficiency, including predictive analytics, real-time optimisation, and renewable energy integration. We further explore applications across industries, real-world case studies, policy recommendations, and potential future directions. AI’s role in transforming static buildings into smart, sustainable ecosystems is no longer theoretical—it’s an emerging reality with measurable benefits for operational costs, occupant comfort, and environmental sustainability.