Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns
AbstractForecasting 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Iglesias, F.; Kastner, W. Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns. Energies 2013, 6, 579-597.
Iglesias F, Kastner W. Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns. Energies. 2013; 6(2):579-597.Chicago/Turabian Style
Iglesias, Félix; Kastner, Wolfgang. 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns." Energies 6, no. 2: 579-597.