Accurate day-ahead wind-power forecasts are becoming more important as wind generation grows, according to a new arXiv preprint. The authors propose a multi-site deep neural network approach trained on data from onshore wind farms across Great Britain, aiming to support system operations, market activity, and grid balancing.
The abstract highlights the value of sharing information across multiple wind farms rather than forecasting each site in isolation. However, the visible portion of the abstract stops after describing the motivation and the training setup, so specific model architectures, performance metrics, and comparisons with baseline methods are not available in the source text.
As it stands, the paper signals a continued push toward deep learning for renewable-energy forecasting, but readers will need to consult the full manuscript for evidence of effectiveness.