TY - GEN

T1 - A performance-based clustering method for retrofit management of building stocks

AU - Geyer, Philipp Florian

AU - Schlüter, Arno

AU - Cisar, Sasha

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84912550558&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84912550558

BT - EG-ICE 2014, European Group for Intelligent Computing in Engineering - 21st International Workshop

T2 - 21st International Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2014

Y2 - 16 July 2014 through 18 July 2014

ER -