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In this paper, a digital twin-based assessment framework is proposed to determine which energy-saving technologies and strategies will work best in existing buildings. The proposed framework is based on a digital twin that integrates the existing building’s hardware system, the building’s operational schedule database, and a probabilistic model of occupant behavior. A digital model was constructed based on field measurements and database integration for a case study involving nine university buildings and 55 classrooms. As a result, in the classrooms involved in the case study, the lighting was turned on in the absence of occupants for an average of 10.7 h a day. The results indicate that it is very important to turn off the lights after the last hour of use in university classrooms in South Korea and that it is possible to reduce power consumption by more than 60% by employing an off strategy involving a passive infrared sensor or manager. Additionally, LED lighting in most classrooms is over-designed, which indicates that 46% of the energy consumed can be saved by adjusting the luminance level to an appropriate range.
Seo et al. (Mon,) studied this question.