University campuses represent complex facilities with diverse building types exhibiting distinct energy consumption patterns. However, current evaluation systems fail to account for building-specific operational characteristics, operating schedules, occupancy patterns, and functional purposes, resulting in systematically unfair assessments. This study analyses hourly energy data from 47 buildings at Yonsei University Sinchon Campus (2023). Based on this analysis, it develops a hierarchical classification framework for Building Energy Influential Factors (BEIF) and proposes comprehensive prediction matrices for fair energy evaluation. Analysis revealed that university building energy consumption comprises approximately 60% baseload energy (research equipment, servers, infrastructure) and 40% building energy (HVAC, lighting, water heating), with engineering research facilities consuming 3.6 times more energy per area than humanities facilities. Most significantly, incorporating operating hours into evaluation fundamentally altered building assessments. Buildings that appeared energy-efficient under conventional area-based total consumption metrics were revealed as high energy intensity consumers when time-weighted indicators were applied. Conversely, facilities with extended operating hours demonstrated appropriate efficiency when temporal context was incorporated. (Wh/m2 • h versus conventional kWh/m2 • yr). It also develops a three-tier hierarchical classification: Tier 1 (Primary Factors), Tier 2 (Integrated Factors), and Tier 3 (Prediction/Evaluation Models). Through systematic factor combinations, 17 potential evaluation scenarios are identified with varying accuracy levels and practical applications. Furthermore, a 3×3 assessment framework is proposed, mapping Infrastructure Levels against Management Goals. This framework provides pathways for institutions to self-assess their capabilities and select appropriate evaluation approaches. The research provides a practical foundation for developing equitable university energy management policies and advancing toward carbon-neutral smart campus implementation using existing verified data sources.
Cho et al. (Tue,) studied this question.