Details
| Original language | English |
|---|---|
| Title of host publication | Smart Cities |
| Subtitle of host publication | Foundations, Principles, and Applications |
| Editors | Houbing Song, Ravi Srinivasan, Tamim Sookoor, Sabina Jeschke |
| Publisher | Wiley-Blackwell |
| Pages | 437-472 |
| Number of pages | 36 |
| ISBN (electronic) | 9781119226444 |
| ISBN (print) | 9781119226390 |
| Publication status | Published - 30 Jun 2017 |
| Externally published | Yes |
Abstract
Keywords
- Automated network identification, Building stock management, Data mining methods, Energy efficiency measures, Energy retrofits, Fuzzy reasoning, Hierarchical agglomerative clustering, Smart city contexts, Smart energy networks, Strategic planning
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
Sustainable Development Goals
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Smart Cities: Foundations, Principles, and Applications. ed. / Houbing Song; Ravi Srinivasan; Tamim Sookoor; Sabina Jeschke. Wiley-Blackwell, 2017. p. 437-472.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks
AU - Geyer, Philipp Florian
AU - Schlueter, Arno
N1 - Publisher Copyright: © 2017 John Wiley & Sons, Inc. All rights reserved.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - This chapter discusses the application of hierarchical agglomerative clustering and fuzzy reasoning as data mining methods for the building stock management and strategic planning. It familiarizes the reader with data mining methods for the development of effective retrofit strategies for a building stock, including energy efficiency measures (EEMs) and automated network identification (ANI) for smart energy networks. Applied data mining methods identify groups of buildings for exactly defined purposes, that is, to find buildings that react similarly to retrofitting measures. This allows for the development of intelligent systemic strategies instead of isolated approaches to individual buildings. The chapter also identifies the benefits and methodological differences between sparse information approaches, that is, the type-age classification, and novel approaches based on information available from building catalogs and databases, measurements, as well as data mining methods in smart city contexts.
AB - This chapter discusses the application of hierarchical agglomerative clustering and fuzzy reasoning as data mining methods for the building stock management and strategic planning. It familiarizes the reader with data mining methods for the development of effective retrofit strategies for a building stock, including energy efficiency measures (EEMs) and automated network identification (ANI) for smart energy networks. Applied data mining methods identify groups of buildings for exactly defined purposes, that is, to find buildings that react similarly to retrofitting measures. This allows for the development of intelligent systemic strategies instead of isolated approaches to individual buildings. The chapter also identifies the benefits and methodological differences between sparse information approaches, that is, the type-age classification, and novel approaches based on information available from building catalogs and databases, measurements, as well as data mining methods in smart city contexts.
KW - Automated network identification
KW - Building stock management
KW - Data mining methods
KW - Energy efficiency measures
KW - Energy retrofits
KW - Fuzzy reasoning
KW - Hierarchical agglomerative clustering
KW - Smart city contexts
KW - Smart energy networks
KW - Strategic planning
UR - http://www.scopus.com/inward/record.url?scp=85052631825&partnerID=8YFLogxK
U2 - 10.1002/9781119226444.ch16
DO - 10.1002/9781119226444.ch16
M3 - Contribution to book/anthology
AN - SCOPUS:85052631825
SN - 9781119226390
SP - 437
EP - 472
BT - Smart Cities
A2 - Song, Houbing
A2 - Srinivasan, Ravi
A2 - Sookoor, Tamim
A2 - Jeschke, Sabina
PB - Wiley-Blackwell
ER -