Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential
Federico Amato,
Fabian Guignard,
Alina Walch
et al.
Abstract:The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable and complex to model, the quantification of the spatio-temporal variation of wind power and the related uncertainty is highly relevant for energy planners and policy-makers. Machine Learning has become a popular tool to perform wind-speed and power predictions. However, the existing approaches have several limitations. These include (i… Show more
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