Abstract
Abstract. Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. 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 Estimation (BECE). Despite extensive research, BIM is less used in BECE 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. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.
Highlights
The Building Information modeling (BIM) describes the physical characteristics of building elements utilizing their threedimensional (3D) geometry, semantic, and topology data
Industry Foundation Classes (IFC) provides the interoperability of BIM data across construction, engineering, and architecture domains that share the building information to serve at different applications, e.g., building security management, facility management, emergency pathfinding, and energy efficiency (Chong, Lee and Wang, 2017)
The proposed algorithm improves the accuracy of the Building Energy Consumption Estimation (BECE) analysis because of utilizing the room's properties and their relationship in the learning process, which are extracted from the IFC model
Summary
The Building Information modeling (BIM) describes the physical characteristics of building elements utilizing their threedimensional (3D) geometry, semantic, and topology data. Current states of the arts demonstrate the role of detailed geometrical and semantical information in predicting building energy consumption in both The literature review illustrates the value of 3D information in providing detailed indoor building information useful for datadriven approaches (Fumo, 2014; Bourdeau et al, 2019) In this application, the indoor space concept (known as the IfcSpace class) can be used as a sub-unit of buildings. This paper adopted the GraphSAGE algorithm as a Graph Neural Network (GNN) machine learning model for room-based BECE analysis to apply space properties and topological information in the learning process. The proposed algorithm improves the accuracy of the BECE analysis because of utilizing the room's properties and their relationship in the learning process, which are extracted from the IFC model.
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