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Assessing a Measurement-Oriented Data Management Framework in Energy IoT Applications

Author

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  • Hariom Dhungana

    (Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences, Inndalsveien 28, 5063 Bergen, Norway)

  • Francesco Bellotti

    (Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy)

  • Matteo Fresta

    (Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy)

  • Pragya Dhungana

    (Interconnection and Numbering Section, Nepal Telecommunications Authority, P.O. Box 9754, Kathmandu 44600, Nepal)

  • Riccardo Berta

    (Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy)

Abstract

The Internet of Things (IoT) has enabled the development of various applications for energy, exploiting unprecedented data collection, multi-stage data processing, enhanced awareness, and control of the physical environment. In this context, the availability of tools for efficient development is paramount. This paper explores and validates the use of a generic, flexible, open-source measurement-oriented data collection framework for the energy field, namely Measurify, in the Internet of Things (IoT) context. Based on a literature analysis, we have spotted three domains (namely, vehicular batteries, low voltage (LV) test feeder, and home energy-management system) and defined for each one of them an application (namely: range prediction, power flow analysis, and appliance scheduling), to verify the impact given by the use of the proposed IoT framework. We modeled each one of them with Measurify, mapping the energy field items into the abstract resources provided by the framework. From our experience in the three applications, we highlight the generality of Measurify, with straightforward modeling capabilities and rapid deployment time. We thus argue for the importance for practitioners of using powerful big data management development tools to improve efficiency and effectiveness in the life-cycle of IoT applications, also in the energy domain.

Suggested Citation

  • Hariom Dhungana & Francesco Bellotti & Matteo Fresta & Pragya Dhungana & Riccardo Berta, 2025. "Assessing a Measurement-Oriented Data Management Framework in Energy IoT Applications," Energies, MDPI, vol. 18(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3347-:d:1687810
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    References listed on IDEAS

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    1. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    2. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).
    3. Stute, Judith & Klobasa, Marian, 2024. "How do dynamic electricity tariffs and different grid charge designs interact? - Implications for residential consumers and grid reinforcement requirements," Energy Policy, Elsevier, vol. 189(C).
    4. Mussawir Ul Mehmood & Abasin Ulasyar & Abraiz Khattak & Kashif Imran & Haris Sheh Zad & Shibli Nisar, 2020. "Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems," Energies, MDPI, vol. 13(11), pages 1-19, May.
    5. Bogdan Ovidiu Varga & Arsen Sagoian & Florin Mariasiu, 2019. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges," Energies, MDPI, vol. 12(5), pages 1-19, March.
    6. Hofmann, Matthias & Bjarghov, Sigurd & Sæle, Hanne & Lindberg, Karen Byskov, 2025. "Grid tariff design and peak demand shaving: A comparative tariff analysis with simulated demand response," Energy Policy, Elsevier, vol. 198(C).
    7. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
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