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.
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 |