LAUSR

The rising demand for sustainable smart buildings is increasing the need to come up with an effective decision-making method for building energy usage management. Simulating energy usage data through advanced technologies like Building Information Modeling (BIM) has become possible of late, and data-mining methods that use various data types (e.g., BIM, energy simulation, sensor, and facility management data) will be needed in the future for decision-making support in BIM-based energy management. Proposed herein is a rule-set-based BIM-based data-mining method for data integration and function extension support that considers functional variability and extensibility. Its work effectiveness was verified through the development and creation of a building-energy-management-related scenario and the analysis of the results thereof. Based on the analysis of work effectiveness, we identified that using the proposed method, the effectiveness improved by 14.4 to 20.5 times. The proposed method enables users to gain intuitive BIM-based decision-making information and allows them customize the flexible workflow for changing of use cases.