PSO Based Emotional BPN and RBF Neural Network Models for Wind Speed Prediction
The present research focuses on developing certain proposed machine learning neural network architectures along with certain mathematical criterion and stochastic population based swarm intelligence technique particle swarm optimization inspired by nature behavior to carry out wind speed prediction in renewable energy systems with real time wind farm datasets. In the developed machine learning model, the work concentrated on developing emotional neural network architecture models that are optimized employing the particle swarm optimization approach and the optimized emotional models are employed to carry out effective wind speed prediction for the given real time wind farm data. Four neural network models are proposed – PSO – EBPN (Emotional Back Propagation Neural Network) model, PSO – ERBFNN (Emotional Radial Basis Function Neural Network) model, PSO – EBPN model with hidden neuron criterion and PSO – ERBFNN model with hidden neuron criterion and as well all these four network models are employed to compute the predicted wind speed output. The developed models for wind speed prediction has performed in a better manner avoiding local and global minima problem and as well had a reasonable better convergence rate.
Dr. V. Ranganayaki
Department of Electrical and Electronics Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India.
S. N. Deepa
Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore, Tamil Nadu, India.
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