Details
| Original language | English |
|---|---|
| Title of host publication | EG-ICE 2014, European Group for Intelligent Computing in Engineering - 21st International Workshop |
| Subtitle of host publication | Intelligent Computing in Engineering 2014 |
| ISBN (electronic) | 9780993080708 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 21st International Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2014 - Cardiff, United Kingdom (UK) Duration: 16 Jul 2014 → 18 Jul 2014 |
Abstract
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- General Engineering
Sustainable Development Goals
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EG-ICE 2014, European Group for Intelligent Computing in Engineering - 21st International Workshop: Intelligent Computing in Engineering 2014. 2014.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -