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

被引：0

作者：

De Caro, Fabrizio ^{[1
]}

Andreotti, Amedeo ^{[2
]}

Araneo, Rodolfo ^{[3
]}

Panella, Massimo ^{[4
]}

Vaccaro, Alfredo ^{[1
]}

Villacci, Domenico ^{[1
]}

机构：

[1] Univ Sannio, Dept Engn, Piazza Roma 21, I-82100 Benevento, Italy

[2] Univ Naples Federico II, Dept Elect & Inf Engn, Naples, Italy

[3] Univ Roma La Sapienza, Elect Engn Div DIAEE, Via Eudossiana 18, I-00184 Rome, Italy

[4] Univ Roma La Sapienza, Dept Informat Engn Elect & Telecommun, Via Eudossiana 18, I-00184 Rome, Italy

来源：

2020 20TH IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2020 4TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE) | 2020年

关键词：

Smart grids computing; Knowledge Discovery; Power System Data Compression; High-Performance Computing; ARTIFICIAL-INTELLIGENCE; NEURAL-NETWORKS; POWER; UNCERTAINTY; FRAMEWORK;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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 outlines the potential role of Knowledge Discovery from massive Datasets in smart grid computing, presenting 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.

引用

页数：6

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