Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Building envelope anomaly characterization and simulation using drone time-lapse thermography
Abstract Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ∼2 kWh/m2/year from the baseline. Why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.
Building envelope anomaly characterization and simulation using drone time-lapse thermography
Abstract Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ∼2 kWh/m2/year from the baseline. Why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.
Building envelope anomaly characterization and simulation using drone time-lapse thermography
Rakha, Tarek (Autor:in) / El Masri, Yasser (Autor:in) / Chen, Kaiwen (Autor:in) / Panagoulia, Eleanna (Autor:in) / De Wilde, Pieter (Autor:in)
Energy and Buildings ; 259
29.11.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Envelope , Defects , Unmanned Aerial Systems (UAS) , Thermal Imaging , Simulation , BEM , Building Energy Model , CAD , Computer-Aided Design , CV , Computer Vision , EUI , Energy Use Intensity , F , Fahrenheit , Ft , Foot , HVAC , Heating, Ventilation and Air Conditioning , IR , Infrared , JSON , JavaScript Object Notation , kWh , Kilo-Watt Hour , m , Meters , NDT , Non-Destructive Testing , RGB , Red, Green and Blue , RSI , R-Value in SI Units , SfM , Structure from Motion , SIFT , Scale Invariant Feature Transform , UAS , Unmanned Aerial Systems
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