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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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 languageEnglish
Title of host publicationWorkshop on Intelligent Computing in Engineering
EditorsJimmy Borrmann, André Borrmann, Lucian-Constantin Ungureanu, Timo Hartmann
PublisherUniversitätsverlag der TU Berlin
Number of pages9
ISBN (Print)978-3-7983-3212-6
Publication statusPublished - 6 Aug 2021
Event28th International Workshop on Intelligent Computing in Engineering - Berlin, Germany
Duration: 30 Jun 20212 Jul 2021


Conference28th International Workshop on Intelligent Computing in Engineering
Abbreviated titleEG-ICE 2021
Internet address

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