Application of data mining techniques for building simulation performance prediction analysis

Christoph Morbitzer, Paul Strachan, Catherine Simpson

Research output: Chapter in Book/Report/Conference proceedingChapter

844 Downloads (Pure)

Abstract

Simulation exercises covering long periods (e.g.. annual simulations) can produce large quantities of data. The result data set is often primarily used to determine key performance parameters such as the frequency binning of internal temperatures. Efforts to obtain an understanding for reasons behind the predicted building performance are often only carried out to a limited extent and simulation is therefore not used to its full potential. This paper describes how data mining can be used to enhance the analysis of results obtained from a simulation exercise. It identifies clustering as a particular useful analysis technique and illustrates its potential in enhancing the analysis of building simulation performance predictions.
Original languageEnglish
Title of host publicationProceedings of the 8th International Building Performance Simulation Association Conference
Publication statusPublished - 2003

Keywords

  • architecture
  • building simulation
  • building design
  • building performance
  • data mining

Fingerprint Dive into the research topics of 'Application of data mining techniques for building simulation performance prediction analysis'. Together they form a unique fingerprint.

Cite this