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Energies 2013, 6(2), 579-597; doi:10.3390/en6020579

Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns

Automation Systems Group, Vienna University of Technology, Treitlstr. 1-3/ 4. Floor, Vienna A-1040, Austria
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Received: 22 November 2012 / Revised: 31 December 2012 / Accepted: 11 January 2013 / Published: 24 January 2013
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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Abstract

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. View Full-Text
Keywords: clustering; time-series analysis; similarity measures; pattern discovery; building energy modeling; cluster validity clustering; time-series analysis; similarity measures; pattern discovery; building energy modeling; cluster validity
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Iglesias, F.; Kastner, W. Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns. Energies 2013, 6, 579-597.

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