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CESAR: A Convolutional Echo State AutoencodeR for High‐Resolution Wind Forecasting

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo
Matthew Bonas   +3 more
wiley   +1 more source

Recycling of both resin and fibre from wind turbine blade waste via small molecule-assisted dissolution. [PDF]

open access: yesSci Rep, 2023
Muzyka R   +4 more
europepmc   +1 more source

Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach. [PDF]

open access: yesHeliyon, 2023
Al Noman A   +6 more
europepmc   +1 more source

Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM. [PDF]

open access: yesSensors (Basel), 2022
Tang M   +6 more
europepmc   +1 more source

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