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The parameters identification of a complex OSC nonlinear circuits is very crucial to enhance the performance predictions in terms of output I-V curves of conventional and new generation OSC. It is very difficult the selection of a neural network based algorithm as MLP for a complex OSC circuital modeling then the relative parameters extraction. Thus we propose in the paper accurate nonlinear equations...
Power output fluctuation is a common problem in the various generating systems of renewable energies. The hybrid energy storage system with water and soil is adopted to decrease the fluctuation of solar chimney power generating systems in the present study. The aim of this paper is a cross-comparison between finite element method (FE) and neural networks (NN) simulation results which were produced...
Wind power penetration is increasing more and more in the modern power system and an accurate wind power forecasting is now required to provide an help to the system operator to consider this renewable source in economic scheduling and other typical tasks of the electrical power system or in smart grid applications. The novelty of this Wavelet Recurrent Neural Network (WRNN) based approach consists...
Integrated generation systems (IGSs) are today increasingly considered to exploit renewable energy in order to supply load for remote areas, less developed countries and small isolated communities. The IGS investigated in this paper enclose a PV park with battery energy storage system and this configuration is considered as case study for the campus called Cittadella at the University of Catania....
In the paper is proposed a new neuro-wavelet based approach for the problem of short term load forecasting. The implemented neuro-wavelet based algorithm combines the potential of two soft computing techniques. The strength over other approaches appeared in literature is that firstly the hourly power load data are wavelet processed and then provided as input to an RNN. The obtained simulation results...
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