The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4-2021, 97–100, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-97-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4-2021, 97–100, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-97-2021

  07 Oct 2021

07 Oct 2021

ROOM-BASED ENERGY DEMAND CLASSIFICATION OF BIM DATA USING GRAPH SUPERVISED LEARNING

H. Kiavarz1, M. Jadidi1, A. Rajabifard2, and G. Sohn1 H. Kiavarz et al.
  • 1Geomatics Engineering, Department of Earth & Space Science & Engineering, York University, Toronto, Canada
  • 2Department of Infrastructure Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia

Keywords: BIM, Knowledge Graph, Machine Learning, GraphSAGE, Building Energy Efficiency

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.