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

authored by

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.

External Organisation(s)

KU Leuven
ETH Zurich

Type

Contribution to book/anthology

Pages

437-472

No. of pages

36

Publication date

30.06.2017

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

General Engineering

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy, SDG 11 - Sustainable Cities and Communities

Electronic version(s)

https://doi.org/10.1002/9781119226444.ch16 (Access: Closed)