Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review

Autoren

  • Philipp Florian Geyer
  • Arno Schlueter

Externe Organisationen

  • KU Leuven
  • ETH Zürich
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksSmart Cities
UntertitelFoundations, Principles, and Applications
Herausgeber/-innenHoubing Song, Ravi Srinivasan, Tamim Sookoor, Sabina Jeschke
Herausgeber (Verlag)Wiley-Blackwell
Seiten437-472
Seitenumfang36
ISBN (elektronisch)9781119226444
ISBN (Print)9781119226390
PublikationsstatusVeröffentlicht - 30 Juni 2017
Extern publiziertJa

Abstract

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.

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Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks. / Geyer, Philipp Florian; Schlueter, Arno.
Smart Cities: Foundations, Principles, and Applications. Hrsg. / Houbing Song; Ravi Srinivasan; Tamim Sookoor; Sabina Jeschke. Wiley-Blackwell, 2017. S. 437-472.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review

Geyer, PF & Schlueter, A 2017, Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks. in H Song, R Srinivasan, T Sookoor & S Jeschke (Hrsg.), Smart Cities: Foundations, Principles, and Applications. Wiley-Blackwell, S. 437-472. https://doi.org/10.1002/9781119226444.ch16
Geyer, P. F., & Schlueter, A. (2017). Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks. In H. Song, R. Srinivasan, T. Sookoor, & S. Jeschke (Hrsg.), Smart Cities: Foundations, Principles, and Applications (S. 437-472). Wiley-Blackwell. https://doi.org/10.1002/9781119226444.ch16
Geyer PF, Schlueter A. Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks. in Song H, Srinivasan R, Sookoor T, Jeschke S, Hrsg., Smart Cities: Foundations, Principles, and Applications. Wiley-Blackwell. 2017. S. 437-472 doi: 10.1002/9781119226444.ch16
Geyer, Philipp Florian ; Schlueter, Arno. / Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks. Smart Cities: Foundations, Principles, and Applications. Hrsg. / Houbing Song ; Ravi Srinivasan ; Tamim Sookoor ; Sabina Jeschke. Wiley-Blackwell, 2017. S. 437-472
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