A new design methodology to predict wind farm energy production by means of a spiking neural network–based system

Abstract

In this paper, a spiking neural network–based architecture for the prediction of wind farm energy production is proposed. The model is also able to evaluate the wake effects due to interactions between the elements of a wind farm on the energy production of the whole farm. This method has been applied to a large wind power plant, composed of 28 turbines and 3 anemometric towers, located in the rural area of Vizzini's unicipality in province of Catania, Italy, that is characterised by a complex orography and an extension of 30 km2. For the implementation of this architecture it was used the “NeuCube” simulator. The results show that the presented method can be successfully applied for predictions of wind energy generation in real wind farm also in presence of faults.