high-performance computing | Ricerc@Sapienza

A review of the enabling methodologies for knowledge discovery from smart grids data

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,

A review of the enabling methodologies for knowledge discovery from smart grids data

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.

A Review of the enabling methodologies for knowledge discovery from smart grids data

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.

TeraStat 2

Lingua Italiano

TeraStat2 is an HPC infrastructure developed by the Dipartimento of Scienze Statistiche and hosted by the InfoSapienza IT center of University of Rome - La Sapienza. It provides a general-purpose, massively parallel supercomputing infrastructure for solving large mathematical models on Big Data.