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On the Tradeoff between Energy Data Aggregation and Clustering Quality

Published:12 June 2018Publication History

ABSTRACT

Energy data from industrial facilities is collected with high frequency. The resulting data volumes pose a scalability challenge for subsequent analyses. While data aggregation can be used to address it, the quality of analyses on aggregated data often is unknown. In our work, we propose an experimental design to evaluate the effects of aggregation on clustering energy data.

References

  1. Charu C Aggarwal. 2015. Data Mining: The Textbook. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Olatz Arbelaitz, Ibai Gurrutxaga, Javier Muguerza, Jesús M Pérez, and Inigo Perona. 2013. An extensive comparative study of cluster validity indices. Pattern Recognition 46, 1 (2013), 243--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Gianfranco Chicco. 2012. Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 42, 1 (2012), 68--80.Google ScholarGoogle ScholarCross RefCross Ref
  4. Lawrence Hubert and Phipps Arabie. 1985. Comparing Partitions. J Classification 2, 1 (1985), 193--218.Google ScholarGoogle ScholarCross RefCross Ref
  5. Rishee K Jain, Kevin M Smith, Patricia J Culligan, and John E Taylor. 2014. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy 123 (2014), 168--178.Google ScholarGoogle ScholarCross RefCross Ref
  6. Ling Jin, Doris Lee, Alex Sim, Sam Borgeson, Kesheng Wu, C Anna Spurlock, and Annika Todd. 2017. Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data. Technical Report. LBNL.Google ScholarGoogle Scholar
  7. Eamonn Keogh, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. 2001. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl Inf Syst 3, 3 (2001), 263--286.Google ScholarGoogle ScholarCross RefCross Ref
  8. Eamonn Keogh and Shruti Kasetty. 2003. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Min Knowl Disc 7, 4 (2003), 349--371. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Eamonn J Keogh and Michael J Pazzani. 2000. Scaling up Dynamic Time Warping for Datamining Applications. In KDD. 285--289. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. William M Rand. 1971. Objective Criteria for the Evaluation of Clustering Methods. J Am Stat Assoc 66, 336 (1971), 846--850.Google ScholarGoogle ScholarCross RefCross Ref
  11. Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20 (1987), 53--65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Silke Wagner and Dorothea Wagner. 2007. Comparing Clusterings -- An Overview. Technical Report. Faculty of Informatics, Universität Karlsruhe (TH).Google ScholarGoogle Scholar
  13. Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, and Eamonn Keogh. 2013. Experimental comparison of representation methods and distance measures for time series data. Data Min Knowl Disc 26, 2 (2013), 275--309. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Tri Kurniawan Wijaya, Tanuja Ganu, Dipanjan Chakraborty, Karl Aberer, and Deva P Seetharam. 2014. Consumer Segmentation and Knowledge Extraction from Smart Meter and Survey Data. In ICDM. 226--234.Google ScholarGoogle Scholar

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          cover image ACM Conferences
          e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems
          June 2018
          657 pages
          ISBN:9781450357678
          DOI:10.1145/3208903

          Copyright © 2018 ACM

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          Publication History

          • Published: 12 June 2018

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