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

verfasst von

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.

Externe Organisation(en)

ETH Zürich

Typ

Aufsatz in Konferenzband

Publikationsdatum

2014

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Angewandte Informatik, Allgemeiner Maschinenbau

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie