Fabio Veronesi

About me:

My name is Fabio, I have a Master degree in Geology from the University of Ferrara (Italy) and a PhD in Spatial statistics and Soil science from Cranfield University (UK).

Since October 2014, I am working on a Postdoc at ETH Zurich (Switzerland) in the group of Geoinformation Engineering. Here I am part of the project SCCER-FURIES, which is a highly multidisciplinary project, with several industrial partners. This project aims at guiding policy makers in the process of shifting the current reliance on nuclear energy toward a future of renewables. In FURIES my focus is on using GIS techniques to help planning new pumped hydro storage facilities and for transmission line planning. My latest work in this project was the creation of a GIS algorithm capable of identifying potential new location for pumped hydro storage in the Swiss Alps. The algorithm takes into account the local hydrology and geology to identify areas with high probability of being suitable for planning. Moreover, we ranked the sites based on their economic value and the potential return of investment. This was done in collaboration with the University of Basel and using the results of their market model.
Moreover, I am collaborating in the project AFEM-INFRA, which aims at assessing the future changes in the Swiss energy market caused by the increase in renewable energy production. This is another highly multidisciplinary project in which I am covering the part about wind resource assessment and wind farm planning. In this context I developed a machine learning algorithm capable of reaching an accuracy comparable with much more sophisticated methods.

The results from this work are in press in the journal "Renewable and Sustainable Energy Reviews"
(DOI: 10.1016/j.rser.2015.11.099)

My research interests are in the field of "GIS for Energy Planning". In fact, energy projects, being renewable or fossil based, require careful planning to identify the most suitable location, not only in terms of site suitability and economic return, but also in terms of social acceptance. GIS and spatial statistical analysis can help for each of these tasks. Using the machine learning algorithm I developed, we are now in the process of creating a wind speed and direction map of the world, in which the local uncertainty will be accurately assessed. This will allow planner to locate potential sites in which they will be able to not only the average capacity factor and the rate of return, but also their uncertainty, meaning the amount of economic risk involved for that particular site. Such a map can also be used to address potential problems related to fossil fuels power plants. These would remain in the energy mix for quite some time and GIS can help planners find locations where their social impact is minimized. For example, the wind map can be used to determine which area will be the most affected by fumes, so that the power plant can be built in the optimal place. Human geography is also interested in studying social acceptance, and spatial statistical techniques can also be employed in this regard to address potentially increase the acceptance of the project.

To know more about my expertises please take a look at my CV.

During my spare time I enjoy hiking with my digital camera. To know more about my passion visit the page Photography.