Reconstruction of the Electric Consumption Pattern from Big Data using MapReduce Technique

Authors

  • Esteban Inga Ortega Universidad Politécnica Salesiana
  • Juan Inga Universidad Politécnica Salesiana
  • Estuardo Correa Universidad Politécnica Salesiana
  • Roberto Hincapié Universidad Pontificia Bolivariana

DOI:

https://doi.org/10.29019/enfoqueute.v9n1.220

Keywords:

big data, map reduce, intelligent electrical network

Abstract

The work presents the performance of the MapReduce technique to reconstruct the load curve from a previously stored amount of information coming from smart metering of electrical energy and currently considered as Big Data. The management of information in the stage of an intelligent electrical network considered as a System of Management of Measured Data or MDMS needs reducing the times with respect to the reports that are required in a certain moment for decision making in relation to the electrical demand response. Therefore, this paper proposes the use of MapReduce as a technique to obtain information of the load curve in a suitable time to obtain trends and statistics related to the residential electric pattern.

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Author Biographies

Esteban Inga Ortega, Universidad Politécnica Salesiana

Universidad Politécnica Salesiana

Coordinador Grupo de Investigación en Redes Eléctricas Inteligentes

Director de Carrera de Electricidad

Juan Inga, Universidad Politécnica Salesiana

Universidad Politécnica Salesiana

Grupo de Investigación en Telecomunicaciones

Carrera de Telecomunicaciones

Roberto Hincapié, Universidad Pontificia Bolivariana

Decano de Ingenierías

References

Ahmed, H. (2014). Applying Big Data Analytics for Energy Efficiency. Master’s Programme in International Design Business Management Hussnain, 85.

Arian, M., Ameli, M., Soleimani, V., & Ghazalizadeh, S. Intelligent migration from smart metering to smart grid, 2 2011 IEEE Power Engineering and Automation Conference § (2011). IEEE. https://doi.org/10.1109/PEAM.2011.6134995

Campaña, M., Inga, E., & Hincapié, R. (2017). Optimal placement of universal data aggregation points for smart electric metering based on hybrid wireless. In CEUR Workshop Proceedings (Vol. 1950).

Chihoub, H., & Collet, C. (2016). A Scalability Comparison Study of Data Management Approaches for Smart Metering Systems. 2016 45th International Conference on Parallel Processing (ICPP), 474–483. https://doi.org/10.1109/ICPP.2016.61

Dai, J., Chai, B., Qiu, H., Zhang, B., & Jiang, W. (2016). Probabilistic graphical model based residential energy behavioral analysis on hybrid computing platform. 2016 China International Conference on Electricity Distribution (CICED), (Ciced), 1–4. https://doi.org/10.1109/CICED.2016.7576328

Fanibhare, V., & Dahake, V. (2016). SmartGrids: MapReduce framework using Hadoop. 3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016, 400–405. https://doi.org/10.1109/SPIN.2016.7566727

Grolinger, K., Capretz, M. A. M., & Seewald, L. (2016). Energy consumption prediction with big data: Balancing prediction accuracy and computational resources. Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016, 157–164. https://doi.org/10.1109/BigDataCongress.2016.27

Grolinger, K., Hayes, M., Higashino, W. a., L’Heureux, A., Allison, D. S., & Capretz, M. a. M. (2014). Challenges for MapReduce in Big Data. Proc. of the SERVICES - IEEE World Congress on Services, (Services), 182–189. https://doi.org/10.1109/SERVICES.2014.41

Han, W., & Yang, X. (2016). Security, Privacy, and Anonymity in Computation, Communication, and Storage. Springer, 10066(March 2017), 1–11. https://doi.org/10.1007/978-3-319-49148-6

Inga-Ortega, E., Peralta-Sevilla, A., Hincapie, R. C., Amaya, F., & Tafur Monroy, I. (2015). Optimal dimensioning of FiWi networks over advanced metering infrastructure for the smart grid. 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), 30–35. https://doi.org/10.1109/ISGT-LA.2015.7381125

Inga-ortega, J., Inga-ortega, E., & Gómez, C. (2017). Electrical Load Curve Reconstruction required for Demand Response using Compressed Sensing Techniques. IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America).

Inga, E., Céspedes, S., Hincapié, R., & Cárdenas, A. (2017). Scalable Route Map for Advanced Metering Infrastructure Based on Optimal Routing of Wireless Heterogeneous Networks. IEEE Wireless Communications, (April), 1–8. https://doi.org/10.1109/MWC.2017.1600255

Inga, E., & Hincapié, R. (2016). Matched Channel Allocation for Advanced Metering Infrastructure based on Cognitive Mobile Virtual Network Operator. Latin America Transactions, IEEE (Revista IEEE America Latina), 14(4), 1780–1785.

Inga, J., Inga, E., Hincapié, R., & Cristina, G. (2017). Optimal Planning for Deployment of FiWi Networks based on Hybrid Heuristic Process. Latin America Transactions, IEEE (Revista IEEE America Latina), 15(9), 1684–1690. https://doi.org/10.1109/TLA.2017.8015053

Inga, J., Inga, E., Hincapié, R., & Gómez, C. (2016). Evaluación de la Infraestructura de Medición y la Respuesta de la Demanda. Revista Técnica Energía, (12), 262–269.

Lang, W., & Patel, J. M. (2010). Energy management for MapReduce clusters. Proceedings of the VLDB Endowment, 3, 129–139. https://doi.org/10.14778/1920841.1920862

Lichtensteiger, B., Bjelajac, B., Müller, C., & Wietfeld, C. (2010). RF Mesh Systems for Smart Metering: System Architecture and Performance. Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, 379–384. https://doi.org/10.1109/SMARTGRID.2010.5622071

Liu, R., Kuo, C., Yang, C., Chen, S., & Liu, J. (2016). On Construction of an Energy Monitoring Service Using Big Data Technology for Smart Campus. 2016 7th International Conference on Cloud Computing and Big Data, 81–86. https://doi.org/10.1109/CCBD.2016.66

Liu, X., Golab, L., Golab, W., & Ilyas, I. F. (2015). Benchmarking Smart Meter Data Analytics. Proceedings of the 18th International Conference on Extending Database Technology, 385–396. https://doi.org/10.5441/002/edbt.2015.34

Mashayekhy, L., Mashayekhy, L., Member, S., Nejad, M. M., & Member, S. (2015). Energy-Aware Scheduling of MapReduce Jobs for Big Data Applications Energy-aware Scheduling of MapReduce Jobs for Big Data Applications. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, XX(September), 1–14. https://doi.org/10.1109/TPDS.2014.2358556

Munshi, A. A., & Mohamed, Y. A.-R. I. (2017). Big data framework for analytics in smart grids. Electric Power Systems Research, 151, 369–380. https://doi.org/10.1016/j.epsr.2017.06.006

Networks, U. P. (2017). LONDON DATASTORE. Retrieved from https://data.london.gov.uk/dataset?format=csv

Nghiem, P. P., & Figueira, S. M. (2016). Towards efficient resource provisioning in MapReduce. Journal of Parallel and Distributed Computing, 95, 29–41. https://doi.org/10.1016/j.jpdc.2016.04.001

Peralta, A., Inga, E., & Hincapié, R. (2015). FiWi Network Planning for Smart Metering Based on Multistage Stochastic Programming. Latin America Transactions, IEEE (Revista IEEE America Latina), 13(12), 3838–3843. https://doi.org/10.1109/TLA.2015.7404917

Peralta, A., Inga, E., & Hincapié, R. (2017). Optimal Scalability of FiWi Networks Based on Multistage Stochastic Programming and Policies. Journal of Optical Communications and Networking, 9(12), 1172. https://doi.org/10.1364/JOCN.9.001172

Selamat, A. (2016). Distributed Computing and Artificial Intelligence, 13th International Conference. (Springer, Ed.) (Springer, Vol. 474). Springer. https://doi.org/10.1007/978-3-319-40162-1

Shi, Y., Zhang, K., Cui, L., Liu, L., Zheng, Y., Zhang, S., & Yu, H. (2016). MapReduce short jobs optimization based on resource reuse. Microprocessors and Microsystems, 47, 178–187. https://doi.org/10.1016/j.micpro.2016.05.007

Sieverts, P., & Systems, E. (2015). Streamlining Smart Meter Data Analytics. Technical University Od Denmark.

Vaccaro, A., Troiano, L., Vaccaro, A., & Vitelli, M. C. (2016). On-line smart grids optimization by case-based reasoning on big data On-line Smart Grids Optimization by Case-Based Reasoning on Big Data. 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), (September), 1–6. https://doi.org/10.1109/EESMS.2016.7504842

Wan, Z., Wang, G., Yang, Y., & Shi, S. (2014). SKM: Scalable Key Management for Advanced Metering Infrastructure in Smart Grids. IEEE Transactions on Industrial Electronics, 61(12), 7055–7066. https://doi.org/10.1109/TIE.2014.2331014

Wang, J. (2014). Frontiers in Algorithmics. (Springer, Ed.) (Springer). Springer. https://doi.org/10.1007/978-3-319-19647-3

Yildiz, O., Ibrahim, S., Phuong, T. A., & Antoniu, G. (2015). Chronos: Failure-aware scheduling in shared Hadoop clusters. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, 313–318. https://doi.org/10.1109/BigData.2015.7363770

Zhang, B. (2017). Analysis of residential electricity consumption behavior based on the improved Apriori algorithm. In C. P. L. : C. Press (Ed.), Electromechanical Control Technology and Transportation : Proceedings of the 2nd International Conference on Electromechanical Control Technology and Transportation (ICECTT 2017) (1st ed., pp. 145–149). Zhuhai, China: CRC Press LLC : CRC Press.

Zhou, J., Hu, R. Q., & Qian, Y. (2012). Scalable distributed communication architectures to support advanced metering infrastructure in smart grid. Parallel and Distributed Systems, IEEE Transactions on, 23(9), 1632–1642. https://doi.org/10.1109/TPDS.2012.53

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Published

2018-03-30

How to Cite

Inga Ortega, E., Inga, J., Correa, E., & Hincapié, R. (2018). Reconstruction of the Electric Consumption Pattern from Big Data using MapReduce Technique. Enfoque UTE, 9(1), pp. 177 – 187. https://doi.org/10.29019/enfoqueute.v9n1.220

Issue

Vol. 9 No. 1 (2018)

Section

Automation and Control, Mechatronics, Electromechanics, Automotive

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