Seller, Hannes (2016) Comparing automated methods for identifying areas of critical heat demand in urban space. REAL CORP 2016 – SMART ME UP! How to become and how to stay a Smart City, and does this improve quality of life? Proceedings of 21st International Conference on Urban Planning, Regional Development and Information Society. pp. 171-177.
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Text (Comparing automated methods for identifying areas of critical heat demand in urban space)
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Abstract
In recent years, urban heat supply has shifted to the center of attention of German energy policy. It is believed that heating grids are an important instrument for climate protection. For one, they open up a heat sink (i.e. a circle of heat customers) large enough to be able to take up heat from cogeneration, which needs a certain minimum scale of operation to be economically viable. Secondly, they allow the relatively easy tying-in of renewable energy sources. However, heating grids are not the one-fits-all solution. As heat transport is associated with losses, a minimum heat density in urban space (that is: MWh per hectar urban space) is needed to make a district heating grid lucrative (and, possibly, ecologically worthwhile – depending on the source of the heat). At the same time, given the nature of the heat generator, a larger area served may offer economies of scale. Opportunities to construct small and medium-sized grids often are overlooked, as information about critical parameters like heat density in a neighborhood are not obvious to potential initiators of such grids. This paper offers a comparison of methods to systematically search an urban heat demand map for areas of critical heat density. Urban heat demand maps are now developed by many municipalities; they are usually constructed using electronic cadastre data, combined with an energetic building typology into which the buildings in the cadastre are mapped. Some potentially interesting opportunities for developing district heating grids may be visible to the experienced eye; algorithms that automatically search over the entire heat map may offer yet more insights. As algorithms I apply (1) a tessellation of the city into tiles of comparable size, and (2) a clustering method used to identify hot spots with two different approaches. I use selected neighborhoods in Hamburg to compare the results of both methods.
| Item Type: | Article |
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| Uncontrolled Keywords: | District Heating, Urban Heat Demand, Energy GIS, Energy Planning, Heating Grid |
| Subjects: | G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Depositing User: | REAL CORP Administrator |
| Date Deposited: | 22 Jul 2016 13:36 |
| Last Modified: | 22 Jul 2016 13:36 |
| URI: | http://repository.corp.at/id/eprint/189 |
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