Comparing automated methods for identifying areas of critical heat demand in urban space

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

Spatial aggregation and visualisation of urban heat demand using graph theory.: An example from Hamburg, Germany.

Because of the physical properties of heat energy, information about the spatial pattern of building heat demand is important for designing climate protection measures in the heating sector (efficiency improvements and renewable energy integration). Many cities in Germany currently prepare ‘heat demand cadastres’ – thematic maps, depicting building heat demand. The growing trend towards open data points into the direction of making these cadastres public, so that different actors can make use of them. However, making such data public may violate the legal requirement of protecting private data. We present a way of tackling this problem with an approach for the aggregation of spatially represented heat demand. Using an algorithm based on graph theory, we group buildings such that the tracing of energetic characteristics and behaviour to individuals is rendered unfeasible. Our method also allows additional constraints to be introduced, for example, aggregating with respect to plot boundaries. We discuss how the building groups can be visualised in a map by presenting a method of generating customised geometries for each group. Finally, we present a visualisation of both specific heat demand (in kWh/(m2*a)) and total heat demand (in kWh/a) in one and the same map. This aids the analysis of more complex questions involving energy efficiency and heat supply

Enriching the 3D City-Model for the Simulation of Urban Heat Demand

In this paper we describe the process of enriching the Hamburg 3D City model (3D-Stadtmodell) with energy relevant attributes for the simulation of heat demand. The 3D data of the city provided by the Landesbetrieb Geoinformation und Vermessung (LGV) is a combination of Cadastre footprints and LiDAR data. This combination of data allows the LGV to produce CityGML data with a level of detail 1 (LOD1). We use this data as basis for the computation of urban heat demand. This paper presents the enrichment process of the CityGML data. We make use of the energy application domain extension (ADE) to store the energy relevant data in a standardized format. For the enrichment process we classify the residential building stock into building types. And classify the non residential sector by use. From the building types we extract heat transmission coefficients of building components. With the enriched 3D city model we perform a monthly heat demand estimation of a selected neighbourhood in Hamburg. The aim of this enrichment process is to create a robust but flexible method for the estimation of heat demand at a neighbourhood level with little energy relevant information. This paper presents a method for a quick estimation of the monthly heat demand of a neighbourhood without the need of any extra data input. This approach can be used by the energy and urban planning community for a first estimation of the heat demand used on a given neighbourhood or the entire city. The results from this approach present an urban heat demand model for the city of Hamburg based on the freely available 3D city model data. Possible uses of this approach are: (1) identification of hot spots in the city, (2) creation of base data sets for the simulation of retrofit scenarios, and (3) creation of temporal heat density maps

Assigning Energetic Archetypes to a Digital Cadastre and Estimating Building Heat Demand. An Example from Hamburg, Germany

In view of the relatively large energy consumption of national building stocks, many cities and municipalities start to prepare energetic building stock models to monitor energy efficiency and plan policies at city or regional scales. In many cases, data on individual buildings is not available. A usual approach to this is the “archetype” approach – classifying the building stock into energetic types (archetypes). This classification is usually based on non-energetic properties available in digital cadastres (construction type, year of construction etc.) and can be a large source of error. We present our research into the difficulties and pitfalls associated with such an approach using the city of Hamburg as an example. In the end, we compare the modelled estimates with consumption data at three different levels to evaluate model performance

Towards an agent-based simulation of building stock development for the city of hamburg

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