Picture of a sphere with binary code

Making Strathclyde research discoverable to the world...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. It exposes Strathclyde's world leading Open Access research to many of the world's leading resource discovery tools, and from there onto the screens of researchers around the world.

Explore Strathclyde Open Access research content

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

Full text not available in this repository. (Request a copy from the Strathclyde author)

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.