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Forecasting of wind power production in theNetherlands

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.