Artificial Intelligence for Sustainable Energy Improvements in the Conservation of Cultural Heritage Historic Buildings

Öz

In the conservation process of historic buildings recognized as cultural heritage, the challenge of balancing conservation principles established by international conservation doctrines with energy efficiency has been increasingly emphasized in recent literature. Although artificial intelligence-based approaches such as multi-objective optimization, machine learning, digital twins, and Historic Building Information Modeling (HBIM) offer significant potential for addressing this challenge, comprehensive bibliometric evidence specifically focusing on the intersection of artificial intelligence, sustainable energy improvement, and cultural heritage historic buildings remains limited. To address this gap, this research conducts a comprehensive bibliometric analysis on a dataset of 122 articles retrieved from the Web of Science Core Collection through a PRISMA-based screening process. Using performance analysis and science mapping techniques, the study examines the period between 2013 and 2026. The findings indicate a marked acceleration after 2024, although the 2026 data should be interpreted cautiously because they represent only the records indexed up to the search date. The field appears to be shaped around four major methodological axes: multi-objective optimization, HBIM and digital twin-based modeling, energy-comfort performance assessment, and sustainable retrofit strategies. The concentration pattern validated through Bradford’s Law (k = 3.19; R² = 0.9839) and Lotka’s Law (β = 4.28; R² = 0.9847) identifies Energy and Buildings as the core journal and highlights research systems centered in Italy, China, and Spain as the leading contributors in the field.

Anahtar Kelimeler

• Historic Buildings
• Cultural Heritage
• Conservation
• Sustainability
• Artificial Intelligence
• Energy Efficiency
• Bibliometric Analysis.

Kaynakça

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