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 language | English |
|---|---|
| Title of host publication | Proceedings of the 8th International Building Performance Simulation Association Conference |
| Publication status | Published - 2003 |
Keywords
- architecture
- building simulation
- building design
- building performance
- data mining