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Data mining analysis of building simulation performance data

Morbitzer, Christoph and Strachan, Paul and Simpson, C.J. (2004) Data mining analysis of building simulation performance data. Building Services Engineering Research and Technology, 25 (3). pp. 253-267. ISSN 0143-6244

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Abstract

Detailed simulation studies of building performance can result in large data sets, particularly where statistical information on annual energy or environmental performance is required. Key performance indicators such as the number of hours above a certain temperature can easily be extracted. However, it is difficult for users to explore such datasets and understand the underlying reasons why a building performs in a certain way. This is especially true in climate responsive buildings which involve complex interactions of ventilation, solar gains, internal gains and thermal mass, for example. Data mining techniques have traditionally been employed in the financial and marketing sectors to elicit patterns within the data. This paper describes how the different data mining techniques may be employed in helping to analyse building performance data. Clustering is identified as a particular useful analysis technique and its potential is illustrated through a number of case studies.

Item type: Article
ID code: 6313
Keywords: data mining, building simulation, buildings, architecture, environment, Mechanical engineering and machinery, Architecture, Building and Construction
Subjects: Technology > Mechanical engineering and machinery
Fine Arts > Architecture
Department: Faculty of Engineering > Mechanical and Aerospace Engineering
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 14 Jul 2008
    Last modified: 12 Jun 2014 10:21
    URI: http://strathprints.strath.ac.uk/id/eprint/6313

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