Ricerc@Sapienza

In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection.

n this paper we present an interesting application of the Decision Support System, known as the OCC_System, designed for faults recognition and classification within the real-world Medium Voltage power grid of Rome, Italy, managed by the Azienda Comunale Energia e Ambiente (ACEA) company. Given a historical data set consisting of fault patterns described by heterogeneous features related to endogenous and exogenous factors, the recognition system is trained to classify fault states assigning them a probability of fault.

Due to the increasing amount of sensors and data streams that can be collected in order to monitor electric distribution networks, developing predictive diagnostic systems over Smart Grids demands powerful and scalable algorithms in order to search for regularities in Big Data. In this regards, Evolutive Agent Based Clustering (E-ABC) is a promising framing reference, as it is conceived to orchestrate a swarm of intelligent agents acting as individuals of an evolving population, each performing a random walk on a different subset of patterns.