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Constructing an urban microsimulation model to assess the influence of demographics on heat consumption
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Figures
![](https://microsimulation.pub:8081/iiif/2/00096%2Fijm-00096-fig1.tif/full/full/0/default.jpg)
Model data structure and simulation steps.
The diagram shows the three main databases (a-c) and the additional building typology used for the classification of the building stock (d). In step 4 the synthetic families (e) are merged with the constructed dwelling units (f). The simulation of heat consumption is performed in step number 5.
![](https://microsimulation.pub:8081/iiif/2/00096%2Fijm-00096-fig2.tif/full/617,/0/default.jpg)
Spatial data used in our model.
(a) Statistical areas `Statistische Gebiete’’ containing aggregated sociodemographic information. Statistisches Amt für Hamburg und Schleswig-Holstein (StaNord); and (b) Digital cadaster ALKIS `Amtliches Liegenschaftskatasterinformationssystem’’. Landesbetrieb Geoinformation und Vermessung – Stadt Hamburg (2010).
![](https://microsimulation.pub:8081/iiif/2/00096%2Fijm-00096-fig4.tif/full/full/0/default.jpg)
Performance of the algorithm used for selecting households to fit aggregate values of statistical area 16004.
![](https://microsimulation.pub:8081/iiif/2/00096%2Fijm-00096-fig5.tif/full/617,/0/default.jpg)
Geometrical simplification of selected buildings (left) original geometry; and (right) simplified geometry used as input for the estimation of heat demand.
(left) original geometry; and (right) simplified geometry used as input for the estimation of heat demand
![](https://microsimulation.pub:8081/iiif/2/00096%2Fijm-00096-fig6.tif/full/617,/0/default.jpg)
Comparison between simulated and observed parameters.
These Parameters are used as restrictions to merge the households to the dwelling units in statistical area 16,004. Result after 10e+6 (Selection of households from the micro census) + 10e+6 (merge of households with dwelling units) iterations.
![](https://microsimulation.pub:8081/iiif/2/00096%2Fijm-00096-fig7.tif/full/617,/0/default.jpg)
Comparison between estimated heat demand and simulated heat consumption.
Comparison between heat demand estimated with a heat balance method, using the ``average’’ occupant (vertical axis) and with help of: (a) building typologies; and (b) a heat balance, taking into account occupant influence, induce thought the synthetic simulated demographic characteristics via a spatial microsimulation (horizontal axis). This comparison shows the results of buildings in statistical area 16004.
Tables
Different types of models for energy simulation of buildings.
Model Type\Occupant Type | Static heat balance model (at individual building level) | Dynamic thermal simulation model (at individual building level) | Statistic model relating heat consumption to occupants, at various levels of aggregation, no heat balance imposed |
---|---|---|---|
“Average Occupant” (household comprised of identical norm persons; only their number counts) | Blesl, Kempe, Ohl and Fahl (2007), Deutsches Institut für Normung e. V (2011), Dascalaki, Droutsa, Balaras and Kontoyiannidis (2011), Loga, Diefenbach and Born (2011) | EnergyPlus Development Team (2012), Henden and Lamberts (2011) | |
Household with individualized demographics and behaviour | Chingcuanco and Miller (2012) Munoz & Peters (2014) | Lüdemann (2001), Borgeson and Brager (2008), Page et al. (2008), Mahdavi (2011), Widén et al. (2011). | Scott (1980), Bohi and Zimmerman (1984), Guerra Santin et al. (2009). |
Variables used to fit households from the Microcensus into statistical areas.
Constructed variables | |
---|---|
Variable ID | Variable Name |
Average household size (No. of individuals per dwelling unit) | |
Number of single households | |
Number of households with children | |
Number of single parent households | |
Variables directly taken from the micro-census | Variable Name |
ef455 | In what year was your home built? |
ef451 | How many dwelling units has the building in which you are living? |
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Source: MIKROZENSUS 2002 Statistisches Bundesamt (2002). Variable names: translation by the authors.
Variables used for the allocation of the synthetic households into the semi-synthetic dwelling units.
Living standard of the household | |
---|---|
Variable ID | Variable Name |
ef451 | How many dwelling units has the building in which you are living? |
ef453 | What is the floor area of the dwelling unit? |
ef455 | In what year was your home built? |
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Source: MIKROZENSUS 2002 Statistisches Bundesamt (2002). Variable names: translation by the authors.
Variables used to estimate the time at home for the single individuals.
Labor participation | |
---|---|
Variable ID | Variable Name |
ef95 | Employment status in the reference week |
ef138 | Full-time / part-time job |
ef141 | Full-time / part-time job |
ef147 | Work on Saturday (February until April) |
ef148 | Work on Sundays and public holidays (February until April) |
ef149 | Evening work (between 6 p.m and 11 p.m) (February until April) |
ef150 | Night work (between 11 p.m and 6 a.m) (February until April) |
ef151 | Night work hours (between 11 p.m and 6 a.m) (February until April) |
ef163 | Home office (February until April) |
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Source: MIKROZENSUS 2002 Statistisches Bundesamt (2002). Variable names: translation by the authors.
Rules to select input variables to the heat balance model based on the average working hours in the building.
Computed work time (wk) | qi[W/m2] | Ti[C°] | n[h−1] | Possible occupant type | ||
---|---|---|---|---|---|---|
wt = | > 10 | 3 | 18 | 0.3 | single household, employed | |
wt = ≤ 9 | ∧ | > 8 | 6 | 19 | 0.4 | both parents employed |
wt = ≤ 8 | ∧ | > 4 | 5 | 20 | 0.5 | “average” occupant |
wt = ≤ 4 | ∧ | > 1 | 6 | 21 | 0.6 | part time |
wt = ≤ 1 | 7 | 22 | 0.7 | unemployed |
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qi[W /m2] = Internal heat emissions; Ti[C°] = Internal temperature; and n[h−1] = Air exchange rate.
Description of some parameters of statistical area 16004 in comparison of the average value for the city of Hamburg.
Statistical area 16004 | Hamburg | Δ | |
---|---|---|---|
Size of household [# individuals per household] | 2.0 | 1.8 | 0.2 |
Share of foreign nationals [%] | 45.1 | 13.6 | 31.5 |
Share of household with kids [%] | 26.7 | 16.9 | 9.8 |
Share of unemployed residents*[%] | 12.2 | 4.8 | 7.4 |
Share of single person households [%] | 49.8 | 51.2 | 1.4 |
Number of private cars per 1000 residents [cars] | 180 | 353 | 172 |
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*Residents between the ages of 15 and 65.