Abstract
In nowadays energy markets, suppliers are encouraged to model the electricity consumption behavior of their customers in order to improve the quality of service and provide better products with lower investment and operating costs. New load models to support power system are required to mitigate scalability issues, especially considering the increasing penetration of distributed energy resources, varying load demands, and large volumes of data from smart meters. Smart metering allows obtaining detailed measures of the power consumption in the form of large time series that encode load curves. Clustering methods are applied to group costumers according to their similarity, by extracting characteristics of their behavior. Traditional computing approaches are not efficient to deal with the aforementioned problem, particularly when it must be solved in real time. This article proposes applying distributed computing and statistical learning methods to the problem of load curves classification of electricity consumers, applying the Map-Reduce model over the Hadoop framework. A case study, using real representative smart meter data from Uruguay is presented. The obtained results validate the stability and robustness of the approach. The main findings suggest that distributed computing can help electricity companies to deal with large volumes of data in order to improve energy management, provide services to consumers, and support modern smart grid technologies.
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Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20(1), 1–48 (2013)
Amri, Y., Lailatul, A., Fatmawati, F., Setiani, N., Rani, S.: Analysis clustering of electricity usage profile using k-means algorithm. IOP Conf. Ser.: Mater. Sci. Eng. 105, 12–20 (2016)
Curi, M.E., et al.: Single and multiobjective evolutionary algorithms for clustering biomedical information with unknown number of clusters. In: Korošec, P., Melab, N., Talbi, E.-G. (eds.) BIOMA 2018. LNCS, vol. 10835, pp. 100–112. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91641-5_9
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Ekanayake, J., Jenkins, N., Liyanage, K., Wu, J., Yokoyama, A.: Smart Grid: Technology and Applications. Wiley, New York (2012)
Foster, I.: Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering. Addison-Wesley Longman, Boston (1995)
Grandjean, A., Adnot, J., Binet, G.: A review and an analysis of the residential electric load curve models. Renew. Sustain. Energy Rev. 16(9), 6539–6565 (2012)
Laurinec, P., Lucká, M.: Clustering-based forecasting method for individual consumers electricity load using time series representations. Open Comput. Sci. 8(1), 38–50 (2018)
Malcon, J., Sardi, G., Carnelli, E., Franco, R.: Smart management of transmission network in UTE. In: Innovative Smart Grid Technologies Latin America (2015)
Momoh, J.: Smart Grid: Fundamentals of Design and Analysis. Wiley-IEEE (2012)
Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)
Nesmachnow, S., Iturriaga, S.: Cluster-UY: scientific HPC in Uruguay. In: International Supercomputing in Mexico (2019)
Nesmachnow, S., et al.: Demand response and ancillary services for super-computing and datacenters. In: International Supercomputing in México, pp. 1–15 (2019)
Paterakisa, N., Erdinc, O., Catalão, J.: An overview of demand response: key-elements and international experience. Renew. Sustain. Energy Rev. 69, 871–891 (2017)
Räsanen, T., Voukantsis, D., Niska, H., Karatzas, K., Kolehmainen, M.: Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energy 87(11), 3538–3545 (2010)
Rhodes, J., Cole, W., Upshaw, C., Edgar, T., Webber, M.: Clustering analysis of residential electricity demand profiles. Appl. Energy 135, 461–471 (2014)
Shaukat, N., et al.: A survey on consumers empowerment, communication technologies, and renewable generation penetration within smart grid. Renew. Sustain. Energy Rev. 81, 1453–1475 (2018)
Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop distributed file system. In: IEEE 26th Symposium on Mass Storage Systems and Technologies, pp. 1–10 (2010)
Sun, M., Konstantelos, I., Strbac, G.: C-vine copula mixture model for clustering of residential electrical load pattern data. IEEE Trans. Power Syst. 32(3), 2382–2393 (2017)
Thorndike, R.: Who belong in the family. Psychometrika 18(4), 267–276 (1953)
White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Inc., Sebastopol (2009)
Zhao, W., Ma, H., He, Q.: Parallel k-means clustering based on MapReduce. In: IEEE International Conference on Cloud Computing, pp. 674–679 (2009)
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Garabedian, S., Porteiro, R., Nesmachnow, S. (2019). Generation and Classification of Energy Load Curves Using a Distributed MapReduce Approach. In: Torres, M., Klapp, J. (eds) Supercomputing. ISUM 2019. Communications in Computer and Information Science, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-38043-4_1
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DOI: https://doi.org/10.1007/978-3-030-38043-4_1
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