A new design methodology to predict wind farm energy production by means of a spiking neural network–based system. (10th July 2017)

• Record Type: Journal Article
• Authors: Brusca, Sebastian; Capizzi, Giacomo; Lo Sciuto, Grazia; Susi, Gianluca
• Other Names: Laudani Antonino guestEditor.; Fulginei Francesco Riganti guestEditor.; Salvini Alessandro guestEditor.
• Is Part Of: International journal of numerical modelling. Volume 32:Number 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-10
• Subjects: NeuCube -- spiking neural network -- wind -- wind power forecasting -- wind power plant; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2267
• Languages: English
• ISSNs: 0894-3370

Abstract

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 municipality in province of Catania, Italy, that is characterised by a complex orography and an extension of 30 k m 2 . 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.