TY - JOUR

T1 - Application of clustering for the development of retrofit strategies for large building stocks

AU - Geyer, Philipp Florian

AU - Schlueter, Arno

AU - Cisar, Sasha

N1 - Funding Information: This work is partly funded by the Swiss Commission for Technology and Innovation (CTI). We furthermore acknowledge the contributions of the research groups of ETH Chairs of Architecture and Urban Design, Prof. Kees Christiaanse, Michael Wagner (project lead), Building Physics, Prof. Dr. Jan Carmeliet, Dr. Kristina Orehounig and the ETH Institute for Environmental Engineering, Prof. Dr. Stefanie Hellweg, and Niko Heeren, Andreas Frömelt and Bernhard Steubing for their contributions to the building database and data analysis.

PY - 2017/1

Y1 - 2017/1

N2 - 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.

AB - 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.

KW - Algorithmic clustering

KW - Building retrofitting

KW - Data mining

KW - Strategic building stock management

UR - http://www.scopus.com/inward/record.url?scp=84960391394&partnerID=8YFLogxK

U2 - 10.1016/j.aei.2016.02.001

DO - 10.1016/j.aei.2016.02.001

M3 - Article

AN - SCOPUS:84960391394

VL - 31

SP - 32

EP - 47

JO - Advanced engineering informatics

JF - Advanced engineering informatics

SN - 1474-0346

ER -