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

Fabrizio De Caro, Amedeo Andreotti, Rodolfo Araneo, Massimo Panella, Antonello Rosato, Alfredo Vaccaro and Domenico Villacci
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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

Energies, 2020, vol. 13, issue 24, 1-25

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.

Keywords: smart grids computing; knowledge discovery; power system data compression, high-performance computing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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