The development of a two day ahead power forecasting model for an offshore windpark

Abstract

The growing fleet of offshore wind farms in the North Sea imposes a lot of stress on the energy grid due to its volatile power production. Accurate forecasting models that can predict the power generation of these wind parks could help grid operators to keep the energy grid stable and reduce the re-dispatch of energy. Therefore the objective of this thesis was to find how to make an accurate power forecasting model for an offshore wind turbine park. To find the most suitable model, five different models were made. The first model tested was a physical model which calculates the power output through a deterministic approach. Four other models were made through the use of machine learning algorithms. A Gradient Boosting model, a support vector regressor, a feed-forward neural network, and a recurrent neural network were built to find out which one was most suitable. The next aspect looked at was what input data could be beneficial to make the power predictions, the numerical weather prediction model ICON-EU was chosen as input data. The support vector regressor algorithm showed the best results with a score of relative mean absolute error 14.35\%, which is the product of the mean absolute error divided by the difference between the minimum and maximum power production of the period in question. Since the performance of the model met the requirements it can be concluded that the forecasting model can be beneficial for the grid operator to reduce the amount of re-dispatched energy.