Room-Based Energy Demand Classification of Bim Data Using Graph Supervised Learning

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.

A graph-based explanatory model for room-based energy efficiency analysis based on BIM data

“…Also, employing the edge's feature in the learning process, the topology information is applied based on the common area and R-Value between two neighbor and target spaces. Different aggregation functions are LSTM (Long Short-Term Memory) aggregator, Pooling aggregator, and Mean aggregator (Kiavarz et al, 2021). We have chosen the Mean aggregator for this research because of its simplicity in implementation.…”