Ramp prediction in wind farms

摘要: Abstract Globally, wind energy has lessened the burden on conventional fossil fuel-based power generation. Wind resource assessment for onshore and offshore farms aids in accurate forecasting analyzing nature of ramp events. Ramp events are scenarios where speed changes over a small amount time leading to large change. From an industrial point view, event short duration is likely cause damage farm connected utility grid. In this chapter, predicted using hybrid machine intelligent techniques such as support vector regression (SVR) its variants, random forest regression, gradient boosted machines sites. A wavelet transform-based signal processing technique used extract features from speed. Results reveal that SVR-based prediction models give best performance. addition, (GBM) predict closer twin (TSVR) model. Furthermore, randomness evaluated by calculating log entropy obtained decomposition empirical model decomposition.