Born in Catania in 1984, he holds a Master degree in Electronic engineering with specialization in automation and control of complex systems.
He started his career in the research sector in the I.N.F.N. (National Institute for Nuclear Physics) involved in the development and implementation of control boards and software systems for Data Acquisition and real time monitoring of data acquired from hundreds of underwater optical and acoustic sensors.
Hired by Enel Distribuzione, the Italian DSO (Distribution System Operator) in 2010, he entered the energy distribution sector managing development projects of the MV/LV electrical grid, analysing and supervising the energy flow utilising Data remotely acquired and ingested into a control and monitoring system as well as supporting the implementation of distributed smart sensors for smart grid projects.
Since 2014 he has been working in the renewable energy sector, for Enel Green Power, as member of the Global Control and monitoring systems unit, managing projects on SCADA systems, Control and Monitoring Rooms development at worldwide level, design and implementation of Incident management systems and processes.
His passion about innovation and new technologies led to involvement with Big Data initiatives, predictive modeling, augmented reality and support to startups and small companies in transforming innovative ideas into business solutions.
In addition, he is also working on his PhD in Computer Science, dealing with Machine Learning techniques and Predictive Maintenance algorithms supported by Big Data infrastrucuture.
From December 2016 he has been selected as Fellow of Enel Foundation, leading research projects in the technology sector, with focus on Big Data, Robotics and Artificial Intelligence.
Fabio's Fellowship research proposes: a novel, comprehensive, and innovative approach in order to automate the inspection and prognostic procedure as well as optimize and execute the daily operation and maintenance activities defining the “plant supervisor of the future”, an autonomous AI system able to manage O&M tasks within the power plants.
Leonardi, F., De Benedetti, M., Messina, F., Santoro, C., & Vasilakos, A. (2018). Anomaly Detection and Predictive Maintenance for photovoltaic Systems. Neurocomputing.
The paper presents a learning approach designed to detect possible anomalies in photovoltaic (PV) systems in order to let an operator plan predictive maintenance interventions. The anomaly detection algorithm presented is based on the comparison between the measured and the predicted values of the AC power production.