Application of data mining techniques for building simulation performance prediction analysis

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.