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This thesis investigated the prediction accuracy of offshore wind power using machine learning and classical time series methods. We used Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel along the Autoregressive (AR) model. Time series cross-validation with data from the Lillgrund Wind Farm and hyperparameter tuning were used to enhance the performance of both models. Thro
