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Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

Author

Listed:
  • Rubén Pérez-Chacón

    (Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
    These authors contributed equally to this work.)

  • José M. Luna-Romera

    (Division of Computer Science, University of Sevilla, ES-41012 Seville, Spain
    These authors contributed equally to this work.)

  • Alicia Troncoso

    (Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain)

  • Francisco Martínez-Álvarez

    (Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain)

  • José C. Riquelme

    (Division of Computer Science, University of Sevilla, ES-41012 Seville, Spain)

Abstract

New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.

Suggested Citation

  • Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:683-:d:136831
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    References listed on IDEAS

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    14. Konrad Henryk Bachanek & Blanka Tundys & Tomasz Wiśniewski & Ewa Puzio & Anna Maroušková, 2021. "Intelligent Street Lighting in a Smart City Concepts—A Direction to Energy Saving in Cities: An Overview and Case Study," Energies, MDPI, vol. 14(11), pages 1-19, May.
    15. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    16. Ana De Las Heras & Amalia Luque-Sendra & Francisco Zamora-Polo, 2020. "Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    17. Paraskevi Giourka & Mark W. J. L. Sanders & Komninos Angelakoglou & Dionysis Pramangioulis & Nikos Nikolopoulos & Dimitrios Rakopoulos & Athanasios Tryferidis & Dimitrios Tzovaras, 2019. "The Smart City Business Model Canvas—A Smart City Business Modeling Framework and Practical Tool," Energies, MDPI, vol. 12(24), pages 1-17, December.
    18. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2018. "Data Science and Big Data in Energy Forecasting," Energies, MDPI, vol. 11(11), pages 1-2, November.
    19. Lolwah Binsaedan & Habib M. Alshuwaikhat & Yusuf A. Aina, 2023. "Developing an Urban Computing Framework for Smart and Sustainable Neighborhoods: A Case Study of Alkhaledia in Jizan City, Saudi Arabia," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    20. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
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    22. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    23. Flah Aymen & Chokri Mahmoudi, 2019. "A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City," Energies, MDPI, vol. 12(5), pages 1-22, March.

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