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A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data

Author

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  • Fabrizio De Caro

    (Department of Engineering, University of Sannio, 82100 Benevento, Italy)

  • Amedeo Andreotti

    (Electrical Engineering Department, University of Naples Federico II, 80125 Naples, Italy)

  • Rodolfo Araneo

    (Electrical Engineering Division of DIAEE, University of Rome “La Sapienza”, 00184 Rome, Italy)

  • Massimo Panella

    (Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, 00184 Rome, Italy)

  • Antonello Rosato

    (Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, 00184 Rome, Italy)

  • Alfredo Vaccaro

    (Department of Engineering, University of Sannio, 82100 Benevento, Italy)

  • Domenico Villacci

    (Department of Engineering, University of Sannio, 82100 Benevento, Italy)

Abstract

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances.

Suggested Citation

  • Fabrizio De Caro & Amedeo Andreotti & Rodolfo Araneo & Massimo Panella & Antonello Rosato & Alfredo Vaccaro & Domenico Villacci, 2020. "A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data," Energies, MDPI, vol. 13(24), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6579-:d:461733
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    References listed on IDEAS

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    1. Fabrizio De Caro & Alfredo Vaccaro & Domenico Villacci, 2017. "Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data," Energies, MDPI, vol. 10(2), pages 1-14, February.
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    3. Fabrizio De Caro & Jacopo De Stefani & Gianluca Bontempi & Alfredo A. Vaccaro & Domenico D. Villacci, 2020. "Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons," ULB Institutional Repository 2013/314435, ULB -- Universite Libre de Bruxelles.
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    1. Gianfranco Di Lorenzo & Erika Stracqualursi & Rodolfo Araneo, 2022. "The Journey Towards the Energy Transition: Perspectives from the International Conference on Environment and Electrical Engineering (EEEIC)," Energies, MDPI, vol. 15(18), pages 1-5, September.

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