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Smart city article details
| Title | Clustering And Fuzzy Reasoning As Data Mining Methods For The Development Of Retrofit Strategies For Building Stocks |
|---|---|
| ID_Doc | 14520 |
| Authors | Geyer P.; Schlueter A. |
| Year | 2017 |
| Published | Smart Cities: Foundations, Principles, and Applications |
| DOI | http://dx.doi.org/10.1002/9781119226444.ch16 |
| 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. © 2017 John Wiley & Sons, Inc. All rights reserved. |
| Author 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 |
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