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