Remote Sensing-Based Characterization of Settlement Structures for Assessing Local Potential of District Heat
Open accessAbstract
In Europe, heating of houses and commercial areas is one of the major contributors to greenhouse gas emissions. When considering the drastic impact of an increasing emission of greenhouse gases as well as the finiteness of fossil resources, the usage of efficient and renewable energy generation technologies has to be increased. In this context, small-scale heating networks are an important technical component, which enable the efficient and sustainable usage of various heat generation technologies. This paper investigates how the potential of district heating for different settlement structures can be assessed. In particular, we analyze in which way remote sensing and GIS data can assist the planning of optimized heat allocation systems. In order to identify the best suited locations, a spatial model is defined to assess the potential for small district heating networks. Within the spatial model, the local heat demand and the economic costs of the necessary heat allocation infrastructure are compared. Therefore, a first and major step is the detailed characterization of the settlement structure by means of remote sensing data. The method is developed on the basis of a test area in the town of Oberhaching in the South of Germany. The results are validated through detailed in situ data sets and demonstrate that the model facilitates both the calculation of the required input parameters and an accurate assessment of the district heating potential. The described method can be transferred to other investigation areas with a larger spatial extent. The study underlines the range of applications for remote sensing-based analyses with respect to energy-related planning issues.
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Geiß et al. used remote sensing data to cluster buildings into types to assess district heat potential in a German town.
“For example, using remote sensing data, Geiß et al. [273] clustered buildings into types to assess the potential of district heat in a German town.”
From Tackling climate change with machine learning (2023) · cites this paper as [273]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Geiß, C., Taubenböck, H., Wurm, M., Esch, T., Nast, M., Schillings, C., & Blaschke, T.
- Affiliations
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- University of Würzburg (DE)
- University of Salzburg (AT)
- Corresponding author
- Geiß, Christian
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2011-07-08
- Volume
- 3
- Issue
- 7
- Pages
- 1447-1471
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2010599066
- Cited by
- 43
- References
- 32
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 6 · 2018: 3 · 2017: 2 · 2016: 4 · 2015: 4 · 2014: 7 · 2013: 4
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- elib (German Aerospace Center) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsches Zentrum für Luft- und Raumfahrt
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Mechanics of Materials › Forest Biomass Utilization and Management
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Keywords
- district heating, spatial modeling, heating demand, energy planning
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