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Distributed Computing and Artificial Intelligence, 13th International Conference pp 231–238Cite as

Finding Electric Energy Consumption Patterns in Big Time Series Data

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 474)

Abstract

In recent years the available volume of information has grown considerably due to the development of new technologies such as the sensor networks or smart meters, and therefore, new algorithms able to deal with big data are necessary. In this work the distributed version of the k-means algorithm in the Apache Spark framework is proposed in order to find patterns from a big time series. Results corresponding to the electricity consumptions for years 2011, 2012 and 2013 for two buildings from a public university are presented and discussed. Finally, the performance of the proposed methodology in relation to the computational time is compared with that of Weka as benchmarking.

Keywords

  • Big data
  • Time series
  • Patterns
  • Clustering

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References

  1. Bahmani, B., Moseley, A., Vattani, R., Kumar, R., Vassilvitskii, S.: Scalable k-means++. Proceedings of the VLDB Endowment 5(7), 622–633 (2012)

    CrossRef  Google Scholar 

  2. Capó, M., Pérez, A., Lozano, J.A.: A recursive k-means initialization algorithm for massive data. In: Proceedings of the Spanish Association for Artificial Intelligence (2015)

    Google Scholar 

  3. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)

    CrossRef  Google Scholar 

  4. Ding, R., Wang, Q., Dan, Y., Fu, Q., Zhang, H., Zhang, D.: Yading: fast clustering of large-scale time series data. Proceedings of the VLDB Endowment 8(5), 473–484 (2015)

    CrossRef  Google Scholar 

  5. Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Zomaya, A.Y., Khalil, I., Sebti, F., Bouras, A.: A survey of clustering algorithms for big data: Taxonomy & empirical analysis. IEEE Transactions on Emerging Topics in Computing 5, 267–279 (2014)

    CrossRef  Google Scholar 

  6. Fernández, A., del Río, S., López, V., Bawakid, A., del Jesús, M.J., Benítez, J.M., Herrera, F.: Big data with cloud computing: an insight on the computing environment, mapreduce, and programming frameworks. WIREs Data Mining and Knowledge Discovery 4(5), 380–409 (2014)

    CrossRef  Google Scholar 

  7. Machine Learning Library (MLlib) for Spark (2015). http://spark.apache.org/docs/latest/mllib-guide.html

  8. Hamstra, M., Karau, H., Zaharia, M., Knwinski, A., Wendell, P.: Learning Spark: Lightning-Fast Big Analytics. O’ Really Media (2015)

    Google Scholar 

  9. Hernández, L., Baladrón, C., Aguiar, J.M., Carro, B., Sánchez-Esguevillas, A.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5, 5215–5228 (2012)

    CrossRef  Google Scholar 

  10. Keyno, H.R.S., Ghaderi, F., Azade, A., Razmi, J.: Forecasting electricity consumption by clustering data in order to decline the periodic variable’s affects and simplification the pattern. Energy Conversion and Management 50(3), 829–836 (2009)

    CrossRef  Google Scholar 

  11. Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C., Riquelme, J.M.: Discovering patterns in electricity price using clustering techniques. In: Proceedings of the International Conference on Renewable Energy and Power Quality, pp. 245–252 (2007)

    Google Scholar 

  12. Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C., Riquelme, J.M.: Partitioning-clustering techniques applied to the electricity price time series. In: LNCS, vol. 4881, pp. 990–991 (2007)

    Google Scholar 

  13. Minelli, M., Chambers, M., Dhiraj, A.: Big Data, Big Analytics: emerging business intelligence and analytics trends for today’s businesses. John Wiley and Sons (2013)

    Google Scholar 

  14. Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data 7(3), 267–279 (2014)

    Google Scholar 

  15. Van Wijk, J.J., Van Selow, E.R.: Cluster and calendar based visualization of time series data. In: Proceedings of the International IEEE Symposium on Information Visualization (1999)

    Google Scholar 

  16. Zhao, W., Ma, H., He, Q.: Parallel k-means clustering based on mapreduce. In: LNCS, vol. 5391, pp. 674–679 (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Computer Science, Universidad Pablo de Olavide, 41013, Seville, Spain

    R. Perez-Chacon, R. L. Talavera-Llames, F. Martinez-Alvarez & A. Troncoso

Authors
  1. R. Perez-Chacon
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  2. R. L. Talavera-Llames
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  3. F. Martinez-Alvarez
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  4. A. Troncoso
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Corresponding author

Correspondence to A. Troncoso .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology Faculty of Engineering, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Faculty of Computer Science & Informatio, Universiti Teknologi Malaysia (UTM) Faculty of Computer Science & Informatio, Baharu, Malaysia

    Ali Semalat

  3. Department of Electronics and Compu, Koszalin University of Technology Department of Electronics and Compu, Koszalin, Poland

    Grzegorz Bocewicz

  4. Faculty of Electrical Engineering and Cs, Kielce University of Technology Faculty of Electrical Engineering and Cs, Kielce, Poland

    Paweł Sitek

  5. Faculty of Engineering and Science, Aalborg University Faculty of Engineering and Science, Aalborg, Denmark

    Izabela E. Nielsen

  6. ETS Ingeniería Informática, University of Sevilla ETS Ingeniería Informática, Sevilla, Spain

    Julián A. García García

  7. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid Departamento de Inteligencia Artificial, Madrid, Spain

    Javier Bajo

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Cite this paper

Perez-Chacon, R., Talavera-Llames, R.L., Martinez-Alvarez, F., Troncoso, A. (2016). Finding Electric Energy Consumption Patterns in Big Time Series Data. In: , et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_25

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  • DOI: https://doi.org/10.1007/978-3-319-40162-1_25

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  • Publisher Name: Springer, Cham

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