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F. Iglesias Vazquez, W. Kastner:
"Analysis of similarity measures in times series clustering for the discovery of building energy patterns";
ENERGIES, 6 (2013), 2; S. 579 - 597.

Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown schemes inherent of the data. The adjustment and parameterization of the whole clustering task is complex and submitted to several uncertainties, being the similarity metric one of the first decisions to be made in order to establish how the distance between two independent vectors must be measured. The present paper checks the effect of similarity measures in the application of clustering for discovering representatives in cases where correlation is supposed to be an important factor to consider, e.g., time series. This is a necessary step for the optimized design and development of efficient clustering-based models, predictors and controllers of time-dependent processes, e.g., building energy consumption patterns. In addition, clustered-vector balance is proposed as a validation technique to compare clustering performances.

clustering; time-series analysis; similarity measures; pattern discovery; building energy modeling; cluster validity

http://dx.doi.org/10.3390/en6020579

http://www.mdpi.com/1996-1073/6/2/579

Projektleitung Wolfgang Kastner:
Gesteigerte Energieeffizienz durch den Einsatz von künstlicher Intelligenz im Haus der Zukunft