elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Collecting Data for Urban Building Energy Modelling by Remote Sensing and Machine Learning

Gorzalka, Philip and Garbasevschi, Oana Mihaela and Schmiedt, Jacob and Droin, Ariane and Linkiewicz, Magdalena Monika and Wurm, Michael and Hoffschmidt, Bernhard (2021) Collecting Data for Urban Building Energy Modelling by Remote Sensing and Machine Learning. In: Proceedings of Building Simulation 2021, pp. 1-8. Building Simulations 2021, 01.09.-03.09., Brügge, Belgien.

[img] PDF
1MB

Abstract

High-quality data on the investigated area is crucial for modelling urban building energy demands, but its availability is often insufficient. We present an approach to acquire (i) building geometries, (ii) their ages, and (iii) their retrofit states. It consists of creating a 3D model from aerial imagery, determining building ages through machine learning, generating a simulation model based on open-source tools, and assessing retrofit states by comparing simulated temperatures with infrared thermography (IRT) measurements. The demonstration on a case study quarter in Berlin shows that heat demand results are comparable to other tools. Using machine learning is already wellsuited to close knowledge gaps regarding building ages. However, retrofit state assessment using IRT was unsatisfactory due to insufficient measurement accuracy and is envisaged for improvement in future research, along with a validation of the approach.

Item URL in elib:https://elib.dlr.de/144798/
Document Type:Conference or Workshop Item (Speech)
Title:Collecting Data for Urban Building Energy Modelling by Remote Sensing and Machine Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Gorzalka, PhilipPhilip.Gorzalka (at) dlr.dehttps://orcid.org/0000-0002-1274-0378
Garbasevschi, Oana MihaelaMihaela.Garbasevschi (at) dlr.dehttps://orcid.org/0000-0003-1175-883X
Schmiedt, JacobJacob.EstevamSchmiedt (at) dlr.dehttps://orcid.org/0000-0002-0794-6769
Droin, ArianeAriane.Droin (at) dlr.deUNSPECIFIED
Linkiewicz, Magdalena MonikaMagdalena.Linkiewicz (at) dlr.deUNSPECIFIED
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Hoffschmidt, BernhardBernhard.Hoffschmidt (at) dlr.deUNSPECIFIED
Date:2021
Journal or Publication Title:Proceedings of Building Simulation 2021
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-8
Status:Published
Keywords:Building Modeling, remote sensing, machine learning
Event Title:Building Simulations 2021
Event Location:Brügge, Belgien
Event Type:international Conference
Event Dates:01.09.-03.09.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Jülich , Oberpfaffenhofen
Institutes and Institutions:Institute of Solar Research > Qualifizierung
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Wurm, Michael
Deposited On:25 Oct 2021 10:13
Last Modified:25 Nov 2021 10:29

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.