ScholarWorks@숙명여자대학교

초록

This study proposes an integrated operational model to transform the facility management of large-scale national art museums into a data-driven intelligent system in the context of the climate crisis and digital transformation. To this end, the study conducts a comparative analysis of leading international cases, such as the Louvre Museum and the Metropolitan Museum of Art, alongside the current status of national art museums in Korea. The analysis reveals that while leading overseas institutions achieve predictive maintenance and energy optimization by integrating energy, environment, and equipment data through AI and digital twin technologies, domestic institutions remain at a stage of simple monitoring characterized by data fragmentation across departments. Accordingly, this study systematizes facility operation data into five core categories— energy, equipment condition, environment, safety, and space occupancy. Based on this, it proposes a four-layer integrated model consisting of data collection, a cloud-based integrated platform, AI analysis and prediction, and operational governance, along with Key Performance Indicators (KPIs). This study is significant in that it extends the discussion of smart museums from exhibition and visitor services to the realms of facility infrastructure and organizational innovation, thereby providing practical guidelines for the sustainable management of national art museums.

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