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Globally, wind energy has lessened the burden on conventional fossil fuelbased power generation. Wind resource assessment for onshore and offshore windfarms aids in accurate forecasting and analyzing nature of ramp events. From anindustrial point of view, a large ramp event in a short time duration is likelyto cause damage to the wind farm connected to the utility grid. In thismanuscript, ramp events are predicted using hybrid machine intelligenttechniques such as Support vector regression (SVR) and its variants, randomforest regression and gradient boosted machines for onshore and offshore windfarm sites. Wavelet transform based signal processing technique is used toextract features from wind speed. Results reveal that SVR based predictionmodels gives the best forecasting performance out of all models. In addition,gradient boosted machines (GBM) predicts ramp events closer to Twin supportvector regression (TSVR) model. Furthermore, the randomness in ramp power isevaluated for onshore and offshore wind farms by calculating log energy entropyof features obtained from wavelet decomposition and empirical modeldecomposition.

Machine Intelligent Techniques for Ramp Event Prediction in Offshore and Onshore Wind Farms