Application of clustering for the development of retrofit strategies for large building stocks

verfasst von

Philipp Florian Geyer, Arno Schlueter, Sasha Cisar

Abstract

In order to reduce energy consumption and emissions from the built environment, it is vital to transform the existing building stock and develop retrofit strategies to achieve energy efficiency and building-integrated renewable energy supply. Compared to developing cost-optimal retrofit strategies for one building, the development of strategies for 100 to up to 10,000 buildings remains a major challenge. This paper presents a method to cluster buildings based on their sensitivity to different retrofit measures, focusing on the cost-effectiveness. Derived from algorithmic clustering and combined with time and cost data, a tailored development of retrofit strategies for large building stocks becomes possible. Improved identification of retrofit measures and strategies, in contrast to the conventional classification based on building type and age, is demonstrated. The method is illustrated, using the data from the case study project ‘Zernez Energia 2020’, which aims to achieve carbon neutrality of a Swiss alpine village.

Externe Organisation(en)

KU Leuven
ETH Zürich

Typ

Artikel

Journal

Advanced engineering informatics

Band

31

Seiten

32-47

Anzahl der Seiten

16

ISSN

1474-0346

Publikationsdatum

01.2017

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Information systems, Artificial intelligence

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://lirias.kuleuven.be/handle/123456789/535433 (Zugang: Offen)
https://doi.org/10.1016/j.aei.2016.02.001 (Zugang: Geschlossen)