Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Conference ‘Smart Energy Regions’ 2016 |
| Place of Publication | Cardiff |
| Publisher | University of Wales Institute, Cardiff |
| Pages | 230-241 |
| ISBN (electronic) | 978-1-899895-23-6 |
| Publication status | Published - 2016 |
| Externally published | Yes |
Abstract
maximize the effect of retrofit to reduce GHG emissions in the given limits of the available investment
means. The paper shows that type-age classifications of buildings are not an appropriate grouping for
strategy development and proposes an algorithmic clustering as grouping method based on the effect of
energy efficiency measures (EEM). This novel clustering method delivers groups of buildings that similarly
respond to retrofit measures and thus provide a good basis to develop efficient large-scale retrofit strategies.
Besides illustrating the method and its benefits, the paper draws conclusions on the transfer of the method
to a regional scale. These conclusions address aspects of the larger heterogeneity of the building stock as
well as data availability, scaling and supply structures as well as the utilization of the results for policy
making.
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of the International Conference ‘Smart Energy Regions’ 2016. Cardiff: University of Wales Institute, Cardiff, 2016. p. 230-241.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Performance-Based Clustering for Building Stock Management at Regional Level
AU - Geyer, Philipp Florian
AU - Schlueter, Arno
PY - 2016
Y1 - 2016
N2 - To facilitate the energy transition, the retrofit of building stocks is a crucial task. A strategy is required tomaximize the effect of retrofit to reduce GHG emissions in the given limits of the available investmentmeans. The paper shows that type-age classifications of buildings are not an appropriate grouping forstrategy development and proposes an algorithmic clustering as grouping method based on the effect ofenergy efficiency measures (EEM). This novel clustering method delivers groups of buildings that similarlyrespond to retrofit measures and thus provide a good basis to develop efficient large-scale retrofit strategies.Besides illustrating the method and its benefits, the paper draws conclusions on the transfer of the methodto a regional scale. These conclusions address aspects of the larger heterogeneity of the building stock aswell as data availability, scaling and supply structures as well as the utilization of the results for policymaking.
AB - To facilitate the energy transition, the retrofit of building stocks is a crucial task. A strategy is required tomaximize the effect of retrofit to reduce GHG emissions in the given limits of the available investmentmeans. The paper shows that type-age classifications of buildings are not an appropriate grouping forstrategy development and proposes an algorithmic clustering as grouping method based on the effect ofenergy efficiency measures (EEM). This novel clustering method delivers groups of buildings that similarlyrespond to retrofit measures and thus provide a good basis to develop efficient large-scale retrofit strategies.Besides illustrating the method and its benefits, the paper draws conclusions on the transfer of the methodto a regional scale. These conclusions address aspects of the larger heterogeneity of the building stock aswell as data availability, scaling and supply structures as well as the utilization of the results for policymaking.
M3 - Conference contribution
SP - 230
EP - 241
BT - Proceedings of the International Conference ‘Smart Energy Regions’ 2016
PB - University of Wales Institute, Cardiff
CY - Cardiff
ER -