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Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand

Author

Listed:
  • M. Esteban Muñoz H.

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

  • Ivan Dochev

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

  • Hannes Seller

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

  • Irene Peters

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

Abstract
We present a procedure for creating a spatially referenced building stock with population living therein ?a synthetic city? for the case of Germany. The level of spatial disaggregation is the European NUTS?3 level for which data from the newest census (2011) exist. Our application is on the estimation of heat demand. We use the German microcensus (2010) which contains both: (a) detailed sociodemographic characteristics of individuals and (b) detailed information on the type of buildings in which these individuals live. With this data we can generate not only a synthetic population but also a synthetic building stock. The microcensus records the construction year and number of dwelling units of buildings. This allow us to classify the buildings for the estimation of heat demand. This procedure has two major advantages: (1) there exist many models for the estimation of heat demand at building level, we can make use of these models, and (2) with the microcensus as the only required data source we are able to estimate heat demand at a spatially disaggregated level for the entire country. We conclude our paper with an internal validation of the microsimulation model by means of the Total Absolute Error T AE and present the first results from this model aggregated at the NUTS?3 level for the entire country. We briefly discuss the observed patters of the results and attempt to hypothesize on the reasons behind this patterns. We also discuss the difficulties of an external validation of this model and how we can address them in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:ijm:journl:v:9:y:2016:i:3:p:66-88
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    References listed on IDEAS

    as
    1. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    2. 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.
    3. Azizur Rahman & Ann Harding & Robert Tanton & Shuangzhe Liu, 2010. "Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 3-22.
    4. David Pritchard & Eric Miller, 2012. "Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously," Transportation, Springer, vol. 39(3), pages 685-704, May.
    5. Caputo, Paola & Costa, Gaia & Ferrari, Simone, 2013. "A supporting method for defining energy strategies in the building sector at urban scale," Energy Policy, Elsevier, vol. 55(C), pages 261-270.
    6. Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
    7. Laurie Brown & Ann Harding, 2002. "Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-6.
    8. Robert Tanton & Yogi Vidyattama & Binod Nepal & Justine McNamara, 2011. "Small area estimation using a reweighting algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 931-951, October.
    9. Singh, Manoj Kumar & Mahapatra, Sadhan & Teller, Jacques, 2013. "An analysis on energy efficiency initiatives in the building stock of Liege, Belgium," Energy Policy, Elsevier, vol. 62(C), pages 729-741.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Heating demand; synthetic building stock; spatial microsimulation; GREGWT;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • 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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