… deeplearning-based evolutionary approach for accurate forecasting of the poweroutput in wind-turbinefarms, … in improving the performance of the forecasting model. In the third phase, …
… Artificial Neural Networks (ANN) machinelearning approach is used to … uses Feed Forward Back Propagation (FFBP) ANN models to predict the poweroutput of a single windturbine. …
… considered in this study is the onshore Yalova windfarm, featuring 36 windturbines with total generation capacity of 54,000 kW according to www.tureb.com.tr/bilgi-bankasi/turkiye-res-…
… features and windpoweroutputsthroughdeeplearning neural networks. The methodology applied here is general and can be utilized to other windturbines or upscaled to windfarms. …
… The windpowerproduction increases rapidly. Having … general use of machinelearning in order to digitalize windpower … -free prediction of windpower will bring windturbines one step …
… -step powerpredictions for multiple WTs in a windfarm in this … through a data reorganization, the DETL framework training … -of-use of deeplearning in windpowerpredictions, we adopt …
… can accurately predict the fatigue loads and poweroutput of … data-driven prediction method usingmachinelearning is developed … for forecasting purposes. The superiority of the selected …
C Sasser, M Yu, R Delgado - Renewable Energy, 2022 - Elsevier
… prediction of a windturbine, it is relevant to test the effectiveness of HHWS, REWS, and … more accurately predicted the poweroutput in comparison to the operational windturbine and …