Ricerc@Sapienza

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.