Performance-Based Clustering for Building Stock Management at Regional Level

authored by

Philipp Florian Geyer, Arno Schlueter

Abstract

To facilitate the energy transition, the retrofit of building stocks is a crucial task. A strategy is required to
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.

External Organisation(s)

KU Leuven
ETH Zurich

Type

Conference contribution

Pages

230-241

Publication date

2016

Publication status

Published

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias1954309&context=SearchWebhook&vid=32KUL_KUL:Lirias&lang=en&search_scope=lirias_profile&adaptor=SearchWebhook&tab=LIRIAS&query=any,contains,LIRIAS1954309&offset=0 (Access: Open)