A performance-based clustering method for retrofit management of building stocks

authored by

Philipp Florian Geyer, Arno Schlüter, Sasha Cisar

Abstract

For reducing energy consumption and emissions of the built environment, it is vital to manage building stocks and develop strategies for measures of energy efficiency and building integrated renewable energy supply. However, to develop strategies for 100 to 10,000 buildings is a major challenge for this strategy development. Therefore, this paper presents a method to cluster buildings on their sensitivity to a set of measures. For the groups derived by algorithmic clustering, a tailored development of retrofit strategies is possible. The method is demonstrated by the data of the research project Zernez Energia 2020, which deals with a Swiss village to become carbon neural.

External Organisation(s)

ETH Zurich

Type

Conference contribution

Publication date

2014

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Science Applications, General Engineering

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy