Ricerc@Sapienza

Modeling and recognizing faults and outages in a real-world power grid is a challenging task, in line with the modern concept of Smart Grids. The availability of Smart Sensors and data networks allows to “x-ray scan” the power grid states. The present paper deals with a recognition system of fault states described by heterogeneous information in the real-world power grid managed by the ACEA company in Italy.

In the modern power grid framework, Renewable Energy Sources must be integrated into the existing energy systems to optimally deal with load, power and electromagnetic imbalance issues. In this context, smart grids have a pivotal role in transforming the aggregation of decentralized power sources. In order to implement these complex systems and to enable such an integration, machine learning techniques must be investigated and adopted where necessary.

By describing an experimentation carried out on a ‘informal’ settlement in the far north east outskirts of the Municipality of Rome, the paper aims to demonstrate in which way outskirts can become an extraordinary opportunity to activate processes of smart urban regeneration.