Forecasting of wind power production in the Netherlands

Wind power has become an important source of power for some countries because wind is renewable, wind power is clean and no pollutants are produced compared to fossil fuels which are mainly used for the generation of energy today. This research has obtained wind power forecasting results from a Random forest, Feed forward neural network and a hybrid model consisting of a combination of unsupervised k-nearest neighbour clustering and a neural network. These results have been compared with the forecasting results obtained from an external organization. Based on the comparison of monthly and average monthly MAPD and RMSPD we have found that the Feed forward neural network and the hybrid model are able to obtain a performance equally or even better compared to the external forecasting for a single turbine. The input parameters that made the difference were the u-vector, v-vector, the use of SCADA data and the wind speed time lag 1. Furthermore, the three forecasting models did perform less good compared to the external forecasting on forecasting wind power generated by a wind farm. Main reasons are because we did not take shadowing effects from other turbines into account and also the lack of fuzzy rules overfitted the neural networks at higher wind speed values. The random forest however was more robust and performed best of the three models.