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

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  • Félix Iglesias

    (Automation Systems Group, Vienna University of Technology, Treitlstr. 1-3/ 4. Floor, Vienna A-1040, Austria)

  • Wolfgang Kastner

    (Automation Systems Group, Vienna University of Technology, Treitlstr. 1-3/ 4. Floor, Vienna A-1040, Austria)

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.

Suggested Citation

  • Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:2:p:579-597:d:23092
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    References listed on IDEAS

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    1. Pan Duan & Kaigui Xie & Tingting Guo & Xiaogang Huang, 2011. "Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques," Energies, MDPI, vol. 4(1), pages 1-12, January.
    2. Räsänen, Teemu & Ruuskanen, Juhani & Kolehmainen, Mikko, 2008. "Reducing energy consumption by using self-organizing maps to create more personalized electricity use information," Applied Energy, Elsevier, vol. 85(9), pages 830-840, September.
    3. Ying-Yi Hong & Ching-Ping Wu, 2012. "Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network," Energies, MDPI, vol. 5(11), pages 1-15, November.
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