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Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps

Author

Listed:
  • Simona-Vasilica Oprea

    (Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Piaţa Romană square, 010374 Bucharest, Romania)

  • Adela Bâra

    (Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Piaţa Romană square, 010374 Bucharest, Romania)

  • Bogdan George Tudorică

    (Department of Cybernetics, Economic Informatics, Finance and Accountancy, Petroleum-Gas University of Ploiești, Bucureşti avenue, 100279 Ploiești, Romania)

  • Gabriela Dobrița (Ene)

    (Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Piaţa Romană square, 010374 Bucharest, Romania)

Abstract

The smart metered electricity consumption data and high dimensional questionnaires provide useful information for designing the tariffs aimed at reducing electricity consumption and peak. The volume of data generated by smart meters for a sample of around four thousand residential consumers requires Not only Structured Query Language (NoSQL) solutions, data management and artificial neural network clustering algorithms, such as Self-Organizing Maps. In this paper, we propose a novel methodology that handles a large volume of data and extracts information from electricity consumption measured at 30 min and from complex questionnaires. Five three-level Time-of-Use tariffs are altered and investigated to minimize the consumers’ payment. Then, input data analysis revealed that the peak consumption is influenced by a segment of consumers that can be targeted to flatten the peak. Based on simulations, more than 23% of the peak consumption can be reduced by shifting it from peak to off-peak hours.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra & Bogdan George Tudorică & Gabriela Dobrița (Ene), 2020. "Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps," Sustainability, MDPI, vol. 12(8), pages 1-30, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3442-:d:349291
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    References listed on IDEAS

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    1. Geelen, Daphne & Reinders, Angèle & Keyson, David, 2013. "Empowering the end-user in smart grids: Recommendations for the design of products and services," Energy Policy, Elsevier, vol. 61(C), pages 151-161.
    2. Adnane Kendel & Nathalie Lazaric, 2015. "The diffusion of smart meters in France: A discussion of the empirical evidence and the implications for smart cities," Post-Print halshs-01246427, HAL.
    3. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    4. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    5. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    6. Vassileva, Iana & Odlare, Monica & Wallin, Fredrik & Dahlquist, Erik, 2012. "The impact of consumers’ feedback preferences on domestic electricity consumption," Applied Energy, Elsevier, vol. 93(C), pages 575-582.
    7. Schleich, Joachim & Klobasa, Marian & Gölz, Sebastian & Brunner, Marc, 2013. "Effects of feedback on residential electricity demand—Findings from a field trial in Austria," Energy Policy, Elsevier, vol. 61(C), pages 1097-1106.
    8. Mizobuchi, Kenichi & Takeuchi, Kenji, 2013. "The influences of financial and non-financial factors on energy-saving behaviour: A field experiment in Japan," Energy Policy, Elsevier, vol. 63(C), pages 775-787.
    9. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    10. repec:aen:journl:ej34-1-04 is not listed on IDEAS
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    Cited by:

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    5. Alexandra-Nicoleta Ciucu-Durnoi & Margareta Stela Florescu & Camelia Delcea, 2023. "Envisioning Romania’s Path to Sustainable Development: A Prognostic Approach," Sustainability, MDPI, vol. 15(17), pages 1-18, August.

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