Francesco Grimaccia received the M.S. and Ph.D. (cum laude) in Electrical Engineering from Politecnico di Milano university in 2003 and 2007 respectively. He is currently a faculty professor with the Department of Energy of the same university and received National Scientific Qualification (Full Professorship) by ASN Commission of Italian Ministry of Research in 2018. Since 2007 he attended a professional program on Data and Models in Engineering, Science and Business at MIT (Boston) and other courses on Project Management, Intellectual Property Protection and Technology Transfer Management. His main research activities are focused on computational intelligence and forecasting procedures applied to different electric energy fields, with unmanned technologies and O&M remote techniques of Renewable Energy Systems. Prof. Grimaccia is a Senior Member of the IEEE, member of the Computational Intelligence Society and serves as reviewer for Italian ANVUR research quality system (VQR 2015-2019). He has authored more than 150 scientific publications receiving a Young Scientist Award, three Best Papers and was invited for a Key Note Speech in to open an IEEE International Conference on Smart System and Technologies.
Lecture: Enabling technologies for future electrification: Data, Algorithms and ICT Infrastructures
Reliable and high quality data analytics represent a key tool for many fields of applied engineering science, including the energy sector. Being able to manage anticipatory knowledge, large data-sets and sensor device information can be used as a competitive advantage to meet global challenges and manage efficient systems.
For instance, due to reducing fossil fuel penetration and strong concerns on climate changes, renewable energy sources are doomed to increase their footprint on the energy mix in the next future. Renewables sources are often highly dependent on variable weather conditions, thus it is crucial to foretell their impact in order to properly manage especially solar and wind with respect to programmable plants. Moreover, computational intelligence techniques are essential also to anticipate electricity market dynamics, both on demand and production sides, but also to foster a smooth integration of large amounts of RES and e-vehicles into the power grid as well as to model and optimize complex energy systems, storage facilities, traffic loads or even predictive maintenance actions in distributed production plants.