OPTIMIZING BUILDING ENERGY EFFICIENCY USING AI AND DIGITAL TECHNOLOGIES: A COMPREHENSIVE ANALYTICAL REVIEW
Abstract
The building sector accounts for approximately 40% of global energy consumption and nearly 33% of energy-related CO₂ emissions [1, 2]. This analytical review explores the evolving role of artificial intelligence (AI) and digital technologies in optimizing building energy efficiency from 2015 to 2024. Through a systematic review methodology conducted in accordance with PRISMA guidelines, we analyzed 527 peer-reviewed articles from the Scopus, Web of Science, and IEEE Xplore databases. Our bibliometric analysis revealed an exponential growth in publications (average annual growth of 32%), with China, the United States, and Germany leading in research output. Content analysis shows that AI-based systems achieve 15-40% energy savings in active buildings, while machine learning models increase prediction accuracy to over 90%. Digital twin technology emerges as a particularly promising approach, enabling real-time monitoring and predictive optimization. Despite technological advances, significant challenges remain in data interoperability, standardization, and implementation costs. This review identifies future research directions, including quantum computing integration, generative AI applications, and blockchain-based energy trading systems, providing scholars and practitioners with a comprehensive understanding of current achievements and future opportunities in this rapidly evolving field.
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