no code implementations • 29 Jul 2021 • Federico Amato, Fabian Guignard, Alina Walch, Nahid Mohajeri, Jean-Louis Scartezzini, Mikhail Kanevski
These include (i) insufficient consideration of spatio-temporal correlations in wind-speed data, (ii) a lack of existing methodologies to quantify the uncertainty of wind speed prediction and its propagation to the wind-power estimation, and (iii) a focus on less than hourly frequencies.
no code implementations • 3 Nov 2020 • Fabian Guignard, Federico Amato, Mikhail Kanevski
Uncertainty quantification is crucial to assess prediction quality of a machine learning model.
1 code implementation • 12 Oct 2020 • Federico Amato, Fabian Guignard, Philippe Jacquet, Mikhail Kanevski
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models.
1 code implementation • 23 Jul 2020 • Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski
As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis.