RT Journal Article
T1 A new design methodology to predict wind farm energy production by means of a spiking neural network–based system
A1 Brusca, Sebastian
A1 Capizzi, Giacomo
A1 Lo Sciuto, Grazia
A1 Susi, Gianluca
AB 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.
PB Wiley
SN 0894-3370
SN 1099-1204
YR 2017
FD 2017-07-10
LK https://hdl.handle.net/20.500.14352/116993
UL https://hdl.handle.net/20.500.14352/116993
LA eng
NO Brusca S, Capizzi G, Lo Sciuto G, Susi G. A new design methodology to predict windfarm energy production by means of a spiking neural network-based system. Int J Numer Model. 2019;32:e2267.https://doi.org/10.1002/jnm.2267
DS Docta Complutense
RD 7 abr 2025