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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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    1. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
    2. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    5. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    6. Fateh Nassim Melzi & Allou Same & Mohamed Haykel Zayani & Latifa Oukhellou, 2017. "A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors," Energies, MDPI, vol. 10(10), pages 1-21, September.
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    4. Muhammad Aslam Jarwar & Sajjad Ali & Ilyoung Chong, 2019. "Microservices Model to Enhance the Availability of Data for Buildings Energy Efficiency Management Services," Energies, MDPI, vol. 12(3), pages 1-27, January.
    5. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
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    7. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
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    9. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.
    10. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    11. Simona Vasilica Oprea & Adela Bâra, 2019. "Big Data Solutions for Efficient Operation of Microgrids," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 266-271, August.
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    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.
    21. 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.
    22. 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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