Bach, Jakob

Böhm, Klemens

Trittenbach, Holger

English

Holger Trittenbach

Jakob Bach

Klemens Böhm

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On the Tradeoff between Energy Data Aggregation and Clustering Quality

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KIT – The Research University in the Helmholtz AssociationDFG Research Training Group 2153 – “Energy Status Data”www.energystatusdata.kit.edu@esdgraduatesOn the Tradeoff between Energy Data Aggregation and Clustering QualityHolger Trittenbach (KIT), Jakob Bach (KIT), Klemens Böhm (KIT)Problem StatementExperimental DesignPreliminary ResultsHigh frequency: energy data collected in second intervals, from many devicesMultiple Measurements: e.g., voltages, currents, harmonic distortionVolume of energy data challenges the scalability of data processing and analysisClustering to discover consumer groups or recurring patternsData reduction by downsampling or aggregation, e.g., average over 15 min Tradeoff between data volume and information contentUnclear how to assess effects of temporal aggregation for energy data clusteringData Set: machine-dependent and grid-dependent electrical quantitiesClustering Algorithm: representative-based, hierarchical, density-basedDissimilarity: lock-step, elastic, complexity-basedAggregation Function: location (max, mean), dispersion (standard deviation), shape (skewness)Aggregation Level: intervals from 1 min up to 6 h Over 43,000 experiment settingsInternal ValidityExternal ValidityClustering Structure: distribution of cluster sizesInternal Validity: quality of clusteringExternal Validity: comparison to target assignmentForecasting: error with clustering as pre-processingDesign SpaceEvaluationSilhouette Coefficient: higher values indicate higher clustering qualityAdjusted Rand Index: high value if cluster assignment for aggregated and unaggregated data are similarResult: location statistics yield best quality, decreasing trend with increasing aggregationFuture WorkBase level validity by aggregation function.In-depth analysis of various evaluation metricsStudy of several electrical quantitiesGuidelines how to evaluate aggregation for domain expertsAcknowledgments: This work was supported by the German Research Foundation (DFG) as part of the Research Training Group GRK 2153: Energy Status Data – Informatics Methods for its Collection, Analysis and Exploitation.

https://publikationen.bibliothek.kit.edu/1000085132/152527474

On the Tradeoff between Energy Data Aggregation and Clustering Quality

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