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

verfasst von

Philipp Florian Geyer, Arno Schlueter

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.

Externe Organisation(en)

KU Leuven
ETH Zürich

Typ

Beitrag in Buch/Sammelwerk

Seiten

437-472

Anzahl der Seiten

36

Publikationsdatum

30.06.2017

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Allgemeiner Maschinenbau

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie, SDG 11 – Nachhaltige Städte und Gemeinschaften

Elektronische Version(en)

https://doi.org/10.1002/9781119226444.ch16 (Zugang: Geschlossen)