TY - JOUR
T1 - LiDAR point-cloud mapping of building façades for building energy performance simulation
AU - O'Donnell, J.
AU - Truong-Hong, Linh
AU - Boyle, N.
AU - Corry, Edward
AU - Cao, Jun
AU - Laefer, Debra F.
N1 - Funding Information:
This work was supported by a Marie Curie FP7 Integration Grant within the European Union 7th Framework Programme project title SuPerB, project number 631617 . The third and last authors are grateful for the generous support of the European Commission through FP7 ERC Consolidator grant, “RETURN: Rethinking Tunnelling for Urban Neighbourhoods”), ERC StG 2012-307836-RETURN . The authors would like to thank Cathal Hoare for proofreading the final manuscript.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/11
Y1 - 2019/11
N2 - Current processes that create Building Energy Performance Simulation (BEPS) models are time consuming and costly, primarily due to the extensive manual inputs required for model population. In particular, generation of geometric inputs for existing building models requires significant manual intervention due to the absence, or outdated nature of available data or digital measurements. Additionally, solutions based on Building Information Modelling (BIM) also require high quality and precise geometrically-based models, which are not typically available for existing buildings. As such, this work introduces a semi-automated BEPS input solution for existing building exteriors that can be integrated with other related technologies (such as BIM or CityGML) and deployed across an entire building stock. Within the overarching approach, a novel sub-process automatically transforms a point cloud obtained from a terrestrial laser scanner into a representation of a building's exterior façade geometry as input data for a BEPS engine. Semantic enrichment is performed manually. This novel solution extends two existing approaches: (1) an angle criterion in boundary detection and (2) a voxelisation representation to improve performance. The use of laser scanning data reduces temporal costs and improves input accuracy for BEPS model generation of existing buildings. The approach is tested herein on two example cases. Vertical and horizontal accuracies of 1% and 7% were generated, respectively, when compared against independently produced, measured drawings. The approach showed variation in accuracy of model generation, particularly for upper floors of the test case buildings. However, the energy impacts resulting from these variations represented less than 1% of the energy consumption for both cases.
AB - Current processes that create Building Energy Performance Simulation (BEPS) models are time consuming and costly, primarily due to the extensive manual inputs required for model population. In particular, generation of geometric inputs for existing building models requires significant manual intervention due to the absence, or outdated nature of available data or digital measurements. Additionally, solutions based on Building Information Modelling (BIM) also require high quality and precise geometrically-based models, which are not typically available for existing buildings. As such, this work introduces a semi-automated BEPS input solution for existing building exteriors that can be integrated with other related technologies (such as BIM or CityGML) and deployed across an entire building stock. Within the overarching approach, a novel sub-process automatically transforms a point cloud obtained from a terrestrial laser scanner into a representation of a building's exterior façade geometry as input data for a BEPS engine. Semantic enrichment is performed manually. This novel solution extends two existing approaches: (1) an angle criterion in boundary detection and (2) a voxelisation representation to improve performance. The use of laser scanning data reduces temporal costs and improves input accuracy for BEPS model generation of existing buildings. The approach is tested herein on two example cases. Vertical and horizontal accuracies of 1% and 7% were generated, respectively, when compared against independently produced, measured drawings. The approach showed variation in accuracy of model generation, particularly for upper floors of the test case buildings. However, the energy impacts resulting from these variations represented less than 1% of the energy consumption for both cases.
KW - Building Energy Performance Simulation (BEPS)
KW - City-scale modelling
KW - Laser scanning
KW - Light Detection And Ranging (LiDAR)
KW - Retrofit
KW - Semi-automated façades generation
UR - http://www.scopus.com/inward/record.url?scp=85069878968&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069878968&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2019.102905
DO - 10.1016/j.autcon.2019.102905
M3 - Article
AN - SCOPUS:85069878968
SN - 0926-5805
VL - 107
JO - Automation in Construction
JF - Automation in Construction
M1 - 102905
ER -