IFC-BIM-Based Critical Energy Consumption Zones Identification in Buildings
Keywords:GIS, BIM, IFC, Energy Efficiency, Machine Learning
A recent wave of interest in building energy consumption and estimation has inbred a considerable amount of energy data and building information, which boosts the data-driven algorithms for broad application throughout the building industry. Also, cities and buildings are increasingly interconnected with modern data models like the 3D city model and Building Information Modelling (BIM) for urban management these days. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in building energy consumption. Unfortunately, despite extensive research, BIM is less used in data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data to find the critical zones in the perspective of energy consumption in different spaces of the building. The preliminary results demonstrated a promising finding of critical zones in buildings that improve pre-construction and post-construction steps.
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