… between wind speed and wind power output. The latter enables transforming the forecasted
… Wind power data from three wind turbines are used to assess the effectiveness of the …

… deep learning-based evolutionary approach for accurate forecasting of the power output in
wind-turbine farms, … in improving the performance of the forecasting model. In the third phase, …

… affects how wind power companies … machine learning techniques to predict wind turbine
power. Applied algorithms include extremely randomized trees, light gradient boosting machine, …

… Artificial Neural Networks (ANN) machine learning approach is used to … uses Feed Forward
Back Propagation (FFBP) ANN models to predict the power output of a single wind turbine. …

… considered in this study is the onshore Yalova wind farm, featuring 36 wind turbines with
total generation capacity of 54,000 kW according to www.tureb.com.tr/bilgi-bankasi/turkiye-res-…

… features and wind power outputs through deep learning neural networks. The methodology
applied here is general and can be utilized to other wind turbines or upscaled to wind farms. …

… The wind power production increases rapidly. Having … general use of machine learning in
order to digitalize wind power … -free prediction of wind power will bring wind turbines one step …

… -step power predictions for multiple WTs in a wind farm in this … through a data reorganization,
the DETL framework training … -of-use of deep learning in wind power predictions, we adopt …

… can accurately predict the fatigue loads and power output of … data-driven prediction method
using machine learning is developed … for forecasting purposes. The superiority of the selected …

… prediction of a wind turbine, it is relevant to test the effectiveness of HHWS, REWS, and …
more accurately predicted the power output in comparison to the operational wind turbine and …