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

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  • 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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    4. Mihaela Muntean & Doina Dănăiaţă & Luminiţa Hurbean & Cornelia Jude, 2021. "A Business Intelligence & Analytics Framework for Clean and Affordable Energy Data Analysis," Sustainability, MDPI, vol. 13(2), pages 1-25, January.

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