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Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption

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  • M. Esteban Muñoz H.

    (Technical Urban Infrastructure Systems Group, HafenCity, University Hamburg)

  • Irene Peters

    (Technical Urban Infrastructure Systems Group, HafenCity, University Hamburg)

Abstract

We present ongoing work on the construction of a spatial microsimulation model to assess the influence of demographics on residential heat consumption for Hamburg, Germany. Demographics are important for urban energy planning as: (1) Buildings are becoming more energy-efficient and building occupant behaviour accounts for a growing share in the variation of consumption; (2) building occupant needs are changing along with demographic change; and (3) the share of small decentralized district heating grids, in which fewer customers mean less averaging out of heterogeneous occupant profiles, is set to play a bigger role in the countrys heat supply. We construct a spatial microdata set for the city of Hamburg (of roughly 1.8 million inhabitants and 370 000 buildings), with households populating geo-referenced buildings, in three steps: (a) Synthesizing the population of small scale statistical areas, comprising up to around 2000 people (we do this by selecting households recorded in the German microcensus and fitting them into the statistical areas); (b) assigning energy relevant properties to the geo-reference buildings from the Hamburg digital cadaster (we do this by making use of a well-established building typology developed for energy assessment) and constructing dwelling units in these buildings; and (c) matching households to the dwelling units in these buildings (which we do again by using household data from the microcensus). This last stepallocating households to buildingsmay be the most interesting and challenging task. As of to date, we use a combinatorial optimization algorithm to achieve this. Once we have a microsimulation model of buildings and households living in them, including their demographic composition, the range of questions that can be explored is immense. The illustration presented here is a simple heat balance computation of individual buildings, using the constructed socio-demographic data and the digital cadaster data as input parameters.

Suggested Citation

  • M. Esteban Muñoz H. & Irene Peters, 2014. "Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 127-157.
    • Handle: RePEc:ijm:journl:v:7:y:2014:i:1:p:127-157
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    Cited by:

    1. Mohamed Khachman & Catherine Morency & Francesco Ciari, 2024. "Integrated multiresolution framework for spatialized population synthesis," Transportation, Springer, vol. 51(3), pages 823-852, June.
    2. Trond Husby & Olga Ivanova & Mark Thissen, 2018. "Simulating the Joint Distribution of Individuals, Households and Dwellings in Small Areas," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 169-190.
    3. M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.

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    More about this item

    Keywords

    Heat consumption; digital cadaster; building stock; spatial microsimulation; combinatorial optimization;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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