Wind Offshore Power Forecasting - A comparative analysis of forecasting accuracy between Machine Learning and classical Time Series models
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
