Abstract
With the development of the power systems in China, there is large volume of basic electricity consumption data accumulated. Mining these data to discover possible consumption patterns and group the users in a more fine-grained way can help the State Grid Corporation to understand users’ personalized and differentiated requirements. In this work, an algorithm called TMeans is proposed to mine the electricity consumption patterns. TMeans improves the classical K-Means algorithm by presenting a set of static and dynamical rules which can dynamically adjust the clustering process according to the statistical features of the clusters, making the process more flexible and practical. Then a MapReduce-based implementation of TMeans is proposed to make itself capable of handling large volume of data efficiently. Through experiment, we first demonstrate that the consumption patterns can be effectively discovered and can be refined to very small granularity through TMeans, and then we show that the MapReduce-based implementation of TMeans can efficiently speed up the clustering process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Smart Grid (July 20, 2013), http://en.wikipedia.org/wiki/Smart_grid
Firth, S., Lomas, K., Wright, A., et al.: Identifying trends in the use of domestic appliances from household electricity consumption measurements. Energy and Buildings 40(5), 926–936 (2008)
Ball, G.H., Hall, D.J.: ISODATA, a novel method of data analysis and pattern classification. Stanford Research Inst., Menlo Park (1965)
Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Proceedings of the 3rd International Conference of Knowledge Discovery and Data Mining, pp. 239–241. The Association for the Advancement of Artificial Intelligence, New York (1998)
Zhao, G., Qu, G.: Analysis and implementation of CLARA algorithm on clustering. Journal of Shandong University of Technology: Sci. & Tech. (2), 45–48 (2006) (in Chinese)
Li, B., Zhao, H., Lv, Z.: Parallel ISODATA Clustering of Remote Sensing Images Based on MapReduce. In: 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 380–383 (2010)
Zhang, S., Liu, J., Zhao, B., et al.: Cloud Computing-Based Analysis on Residential Electricity Consumption Behavior. Power System Technology 37(6), 1542–1546 (2013) (in Chinese)
Borthakur, D.: The hadoop distributed file system: Architecture and design. Hadoop Project Website (2013), http://hadoop.apache.org/common/docs/
Beckel, C., Sadamori, L., Santini, S.: Towards automatic classification of private households using electricity consumption data. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 169–176. ACM (2012)
Chicco, G., Ilie, I.S.: Support vector clustering of electrical load pattern data. IEEE Transactions on Power Systems 24(3), 1619–1628 (2009)
De Silva, D., Yu, X., Alahakoon, D., et al.: A data mining framework for electricity consumption analysis from meter data. IEEE Transactions on Industrial Informatics 7(3), 399–407 (2011)
Zhu, Z., Gu, Z., Wu, J., et al.: Application of cloud computing in electric power system data recovery. Power System Technology 36(9), 44–50 (2012) (in Chinese)
Mu, L., Cui, L., An, N.: Research and practice of cloud computing center for power system. Power System Technology 35(6), 171–175 (2011) (In Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ming, C., Maoyong, C., Yan, W. (2014). Cloud-Based Massive Electricity Data Mining and Consumption Pattern Discovery. In: Huang, Z., Liu, C., He, J., Huang, G. (eds) Web Information Systems Engineering – WISE 2013 Workshops. WISE 2013. Lecture Notes in Computer Science, vol 8182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54370-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-54370-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54369-2
Online ISBN: 978-3-642-54370-8
eBook Packages: Computer ScienceComputer Science (R0)