Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning

Gerald Schweiger; Johannes Exenberger; Avichal Malhotra; Thomas Schranz; Theresa Boiger; C. Van Treeck; James O'Donnell

Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning

Gerald Schweiger, Johannes Exenberger, Avichal Malhotra, Thomas Schranz, Theresa Boiger, C. Van Treeck, James O'Donnell

Institute of Software Technology (7160)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Building energy simulations at district and urban scales are vital to design and operate sustainable energy systems. In many cases, these simulations rely on enrichment methods as the required detailed data on building characteristics are often unavailable. Approaches using machine learning to address this problem have already been proposed in the literature. However, research on this topic is still at an early stage and the question of whether machine learning can offer substantial solutions has not yet been answered. The goal of this work is twofold; based on an expert survey, we identify the main challenges regarding data availability for urban energy simulations.
Furthermore, we identify possibilities of machine learning methods in the field of data enrichment and city information models to offer an initial contribution in defining further research perspectives
in this domain.

Original language	English
Title of host publication	Workshop on Intelligent Computing in Engineering
Editors	Jimmy Borrmann, André Borrmann, Lucian-Constantin Ungureanu, Timo Hartmann
Publisher	Universitätsverlag der TU Berlin
Pages	301-309
Number of pages	9
ISBN (Print)	978-3-7983-3212-6
Publication status	Published - 6 Aug 2021
Event	28th International Workshop on Intelligent Computing in Engineering - Berlin, Germany Duration: 30 Jun 2021 → 2 Jul 2021 https://doi.org/10.14279/depositonce-12021

Conference

Conference	28th International Workshop on Intelligent Computing in Engineering
Abbreviated title	EG-ICE 2021
Country/Territory	Germany
City	Berlin
Period	30/06/21 → 2/07/21
Internet address	https://doi.org/10.14279/depositonce-12021

Cite this

Schweiger, G., Exenberger, J., Malhotra, A., Schranz, T., Boiger, T., Van Treeck, C., & O'Donnell, J. (2021). Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning. In J. Borrmann, A. Borrmann, L-C. Ungureanu, & T. Hartmann (Eds.), Workshop on Intelligent Computing in Engineering (pp. 301-309). Universitätsverlag der TU Berlin.

Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning. / Schweiger, Gerald ; Exenberger, Johannes; Malhotra, Avichal et al.
Workshop on Intelligent Computing in Engineering. ed. / Jimmy Borrmann; André Borrmann; Lucian-Constantin Ungureanu; Timo Hartmann. Universitätsverlag der TU Berlin, 2021. p. 301-309.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Schweiger, G , Exenberger, J, Malhotra, A, Schranz, T, Boiger, T, Van Treeck, C & O'Donnell, J 2021, Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning. in J Borrmann, A Borrmann, L-C Ungureanu & T Hartmann (eds), Workshop on Intelligent Computing in Engineering. Universitätsverlag der TU Berlin, pp. 301-309, 28th International Workshop on Intelligent Computing in Engineering, Berlin, Germany, 30/06/21.

Schweiger G , Exenberger J, Malhotra A, Schranz T, Boiger T, Van Treeck C et al. Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning. In Borrmann J, Borrmann A, Ungureanu L-C, Hartmann T, editors, Workshop on Intelligent Computing in Engineering. Universitätsverlag der TU Berlin. 2021. p. 301-309

Schweiger, Gerald ; Exenberger, Johannes ; Malhotra, Avichal et al. / Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning. Workshop on Intelligent Computing in Engineering. editor / Jimmy Borrmann ; André Borrmann ; Lucian-Constantin Ungureanu ; Timo Hartmann. Universitätsverlag der TU Berlin, 2021. pp. 301-309

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abstract = "Building energy simulations at district and urban scales are vital to design and operate sustainable energy systems. In many cases, these simulations rely on enrichment methods as the required detailed data on building characteristics are often unavailable. Approaches using machine learning to address this problem have already been proposed in the literature. However, research on this topic is still at an early stage and the question of whether machine learning can offer substantial solutions has not yet been answered. The goal of this work is twofold; based on an expert survey, we identify the main challenges regarding data availability for urban energy simulations.Furthermore, we identify possibilities of machine learning methods in the field of data enrichment and city information models to offer an initial contribution in defining further research perspectivesin this domain. ",

author = "Gerald Schweiger and Johannes Exenberger and Avichal Malhotra and Thomas Schranz and Theresa Boiger and {Van Treeck}, C. and James O'Donnell",

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AU - O'Donnell, James

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N2 - Building energy simulations at district and urban scales are vital to design and operate sustainable energy systems. In many cases, these simulations rely on enrichment methods as the required detailed data on building characteristics are often unavailable. Approaches using machine learning to address this problem have already been proposed in the literature. However, research on this topic is still at an early stage and the question of whether machine learning can offer substantial solutions has not yet been answered. The goal of this work is twofold; based on an expert survey, we identify the main challenges regarding data availability for urban energy simulations.Furthermore, we identify possibilities of machine learning methods in the field of data enrichment and city information models to offer an initial contribution in defining further research perspectivesin this domain.

AB - Building energy simulations at district and urban scales are vital to design and operate sustainable energy systems. In many cases, these simulations rely on enrichment methods as the required detailed data on building characteristics are often unavailable. Approaches using machine learning to address this problem have already been proposed in the literature. However, research on this topic is still at an early stage and the question of whether machine learning can offer substantial solutions has not yet been answered. The goal of this work is twofold; based on an expert survey, we identify the main challenges regarding data availability for urban energy simulations.Furthermore, we identify possibilities of machine learning methods in the field of data enrichment and city information models to offer an initial contribution in defining further research perspectivesin this domain.

M3 - Conference paper

SN - 978-3-7983-3212-6

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EP - 309

BT - Workshop on Intelligent Computing in Engineering

A2 - Borrmann, Jimmy

A2 - Borrmann, André

A2 - Ungureanu, Lucian-Constantin

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Y2 - 30 June 2021 through 2 July 2021

ER -

Data shortage for urban energy simulations? An empirical survey on data availability and enrichment methods using machine learning

Abstract

Conference

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