Francesco Grimaccia received the M.S. and Ph.D. (cum laude) in Electrical Engineering from the Politecnico di Milano in 2003 and 2007 respectively. He is currently an Associate Professor with the Department of Energy of the same university. In 2007 he attended the professional program “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 soft computing and forecasting techniques applied to different electric energy fields, with unmanned technologies and Operation&Maintenance of RES plants. Prof. Grimaccia is a Senior Member of the IEEE, member of the Computational Intelligence Society and Vice-President of the AEIT -Milan Section. He has authored more than 120 scientific publications receiving a Young Scientist Award and two IEEE Best Papers in the last two years (2016-2017).
Lecture: Forecasting tools for distributed generation and renewables integration
Reliable and high quality forecasting techniques represent a key analytical tool for many fields of applied engineering science, including especially the sector of energy. Being able to manage anticipatory knowledge can be used as a competitive advantage to meet global challenges and manage complex 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. 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, wind and hydro plants. Moreover, forecasting methods are essential also to anticipate electricity market dynamics, both on demand and production sides, but also to foster a smooth integration of large amounts RES into the grid as well as for the correct estimation of storage systems’ availability, traffic loads or even predictive maintenance actions in energy power plants.