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Conference Paper: A Framework for Constructing Semantic As-is Building Energy Models (BEMs) for Existing Buildings Using Digital Images

TitleA Framework for Constructing Semantic As-is Building Energy Models (BEMs) for Existing Buildings Using Digital Images
Authors
KeywordsBuilding energy model (BEM)
building surface geometry
image-based 3D reconstruction
semantic enrichment
existing buildings
IDF
Issue Date2018
PublisherInternational Association for Automation and Robotics in Construction.
Citation
The 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany, 2018, p. 309-316 How to Cite?
AbstractRetrofits of existing buildings have great potential to reduce global energy consumption and greenhouse gas emissions. Energy modeling of existing buildings, which is commonly conducted to prioritize retrofit strategies, relies on as-is building energy models (BEMs) that represent actual conditions of buildings. Recent efforts have focused on leveraging sensing technologies such as laser scanning and photogrammetry to capture as-is conditions of buildings and developing automatic methods for creating BEMs using the captured data. However, the majority of these efforts are limited to reconstructing 3D facade geometries with poor semantic information for rough BEM use. To this end, this paper presents a framework for an image-based approach to construct complete and semantic as-is BEM geometry models for existing buildings. The framework consists of four modules: 1) the data capture module that collects digital images of building facades and interior spaces and relevant “placement” information for geometry definition; 2) the building surface geometry reconstruction module that recognizes main building components required by BEM and reconstructs their 3D surface geometries from captured images; 3) the semantic enrichment module that adds the required geometry-related semantic relationships among the reconstructed building elements and interior spaces; and 4) the BEM creation module that stores the semantic geometry model in IDF data model. This framework is expected to extend existing research by creating complete (i.e. include not only building facades but also interior spaces) and semantic-rich as-is BEM geometry models.
Persistent Identifierhttp://hdl.handle.net/10722/259870

 

DC FieldValueLanguage
dc.contributor.authorYing, H-
dc.contributor.authorLu, Q-
dc.contributor.authorZhou, H-
dc.contributor.authorLee, SH-
dc.date.accessioned2018-09-03T04:15:23Z-
dc.date.available2018-09-03T04:15:23Z-
dc.date.issued2018-
dc.identifier.citationThe 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany, 2018, p. 309-316-
dc.identifier.urihttp://hdl.handle.net/10722/259870-
dc.description.abstractRetrofits of existing buildings have great potential to reduce global energy consumption and greenhouse gas emissions. Energy modeling of existing buildings, which is commonly conducted to prioritize retrofit strategies, relies on as-is building energy models (BEMs) that represent actual conditions of buildings. Recent efforts have focused on leveraging sensing technologies such as laser scanning and photogrammetry to capture as-is conditions of buildings and developing automatic methods for creating BEMs using the captured data. However, the majority of these efforts are limited to reconstructing 3D facade geometries with poor semantic information for rough BEM use. To this end, this paper presents a framework for an image-based approach to construct complete and semantic as-is BEM geometry models for existing buildings. The framework consists of four modules: 1) the data capture module that collects digital images of building facades and interior spaces and relevant “placement” information for geometry definition; 2) the building surface geometry reconstruction module that recognizes main building components required by BEM and reconstructs their 3D surface geometries from captured images; 3) the semantic enrichment module that adds the required geometry-related semantic relationships among the reconstructed building elements and interior spaces; and 4) the BEM creation module that stores the semantic geometry model in IDF data model. This framework is expected to extend existing research by creating complete (i.e. include not only building facades but also interior spaces) and semantic-rich as-is BEM geometry models.-
dc.languageeng-
dc.publisherInternational Association for Automation and Robotics in Construction.-
dc.relation.ispartof2018 Proceedings of the 35th ISARC-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBuilding energy model (BEM)-
dc.subjectbuilding surface geometry-
dc.subjectimage-based 3D reconstruction-
dc.subjectsemantic enrichment-
dc.subjectexisting buildings-
dc.subjectIDF-
dc.titleA Framework for Constructing Semantic As-is Building Energy Models (BEMs) for Existing Buildings Using Digital Images-
dc.typeConference_Paper-
dc.identifier.emailLee, SH: shlee1@hku.hk-
dc.identifier.authorityLee, SH=rp01910-
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros289728-
dc.identifier.spage309-
dc.identifier.epage316-
dc.publisher.placeBerlin, Germany-

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