The winners have been announced

The first prize of €1.2 million went to Professor José Vilar from Spain, while data scientist Sofie Verrewaere and post-doctoral researcher Yann-Aël Le Borgne, both from Belgium, came in joint second place and won €400,000 each.

The challenge for the applicants was to create software that could predict the likely flow of electricity through a grid taking into account a number of factors including the weather and the generation source (i.e. wind turbines, solar cells, etc). Using a large quantity of data from electricity grids combined with additional data such as weather conditions, applicants had to develop software that could predict the flow of energy through the grid over a six-hour period. The winners were selected based on combined rank of accuracy and speed, with greater weight being given to accuracy.

Why this prize

Many domains of societal or industrial significance, from epidemiology, to climate change, to transportation to energy production and transmission benefit from our ability to examine historical records and predict how the system under study will evolve.

In all these cases, it is not sufficient for predictions be accurate: they also need to be delivered fast enough for corrective action to be applied on the system observed.

This inducement prize also complements the activities of the Big Data Value Public Private Partnership (PPP) which aims to develop Europe's data driven economy and the prospects offered by Big Data technologies.

Challenge

The solution selected demonstrates the ability to analyse extremely large scale collections of structured or geospatial temporal data in a way that is sensitive to the trade-off between the consumption of computational resources and the practical value of the predictions obtained.

This not only results in the more efficient management of those domains in which spatio-temporal predictions are already used, but also in the applications of such predictive methods where today they are not, due to current limitations of speed, scalability, accuracy and resource efficiency.

The analytics tasks and computational environment of the challenge were developed in the framework of SEE.4C-688356 Horizon 2020 Coordination and Support Action.