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.
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Perez-Chacon, R., Talavera-Llames, R.L., Martinez-Alvarez, F., Troncoso, A. (2016). Finding Electric Energy Consumption Patterns in Big Time Series Data. In: Omatu, S., 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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