Abstract
Spatial microsimulation models can be used for the analysis of complex systems. In this paper we make use of a spatial microsimulation model for the estimation of heat demand for Germany at a NUTS–3 level. The presented model creates a synthetic building stock by re-weighting the national microdata sample to small areas (NUTS–3) statistics with help of the GREGWT algorithm. Using the GREGWT method we benchmark the microdata sample to three different aggregation units (a) the building level (i.e. number of buildings); (b) families/dwelling units; and (c) individuals.
The model takes into account the different climate regions defined on the national German 18599-DIN standard. In order to incorporate the climate data into the model, we make use of a quasi steady-state heat transfer model to compute the heat demand of the individual buildings. These type of models require a building geometry for the estimation of heat demand, in this case we do not have information of the individual building geometry but only about the building size, expressed as square meters. We define synthetic geometrical boxes for the computation of heat demand.
The described model is able to represent the national building stock at a microlevel. These type of models are essential for the assessment of policies targeting (a) the reduction of carbon emissions in the construction sector and (b) the increase of energy efficiency on heat distribution grids.
The model takes into account the different climate regions defined on the national German 18599-DIN standard. In order to incorporate the climate data into the model, we make use of a quasi steady-state heat transfer model to compute the heat demand of the individual buildings. These type of models require a building geometry for the estimation of heat demand, in this case we do not have information of the individual building geometry but only about the building size, expressed as square meters. We define synthetic geometrical boxes for the computation of heat demand.
The described model is able to represent the national building stock at a microlevel. These type of models are essential for the assessment of policies targeting (a) the reduction of carbon emissions in the construction sector and (b) the increase of energy efficiency on heat distribution grids.
| Originalsprache | Englisch |
|---|---|
| Titel | Agent Based Modelling of Urban Systems - 1st International Workshop, ABMUS 2016 Held in Conjunction with AAMAS 2016, Revised, Selected, and Invited Papers |
| Redakteure/-innen | Ana Bazzan, Lin Padgham, Kai Nagel, Mohammad-Reza Namazi-Rad, Pascal Perez |
| Seiten | 172-188 |
| Seitenumfang | 17 |
| ISBN (elektronisch) | 978-3-319-51957-9 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 11 Jan. 2017 |
| Veranstaltung | 1st International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016 Held in Conjunction with Autonomous Agents and Multi Agent Systems, AAMAS 2016 - Singapore, Singapur Dauer: 10 Mai 2016 → 10 Mai 2016 |
Publikationsreihe
| Name | Lecture Notes in Computer Science |
|---|---|
| Verlag | Springer International Publishing |
| Nummer | 10051 |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Tagung/Konferenz
| Tagung/Konferenz | 1st International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016 Held in Conjunction with Autonomous Agents and Multi Agent Systems, AAMAS 2016 |
|---|---|
| Land/Gebiet | Singapur |
| Ort | Singapore |
| Zeitraum | 10/05/16 → 10/05/16 |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 07 – Erschwingliche und saubere Energie
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SDG 13 – Klimaschutzmaßnahmen
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