Abstract
Language | English |
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Title of host publication | Proceedings of the 8th International Building Performance Simulation Association Conference |
Publication status | Published - 2003 |
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Keywords
- architecture
- building simulation
- building design
- building performance
- data mining
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Application of data mining techniques for building simulation performance prediction analysis. / Morbitzer, Christoph; Strachan, Paul; Simpson, Catherine.
Proceedings of the 8th International Building Performance Simulation Association Conference. 2003.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Application of data mining techniques for building simulation performance prediction analysis
AU - Morbitzer, Christoph
AU - Strachan, Paul
AU - Simpson, Catherine
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
KW - architecture
KW - building simulation
KW - building design
KW - building performance
KW - data mining
UR - http://www.ibpsa.org/proceedings/BS2003/BS03_0911_918.pdf
M3 - Chapter
BT - Proceedings of the 8th International Building Performance Simulation Association Conference
ER -