Neural network based hybrid computing model for wind speed prediction

• The proposed hybrid model is of definitely a higher standard when compared with conventional MLP, BPN and RBFN models.
• The proposed model exhibits improved accuracy with minimal error.
• The proposed model is very much useful for predicting wind speed in renewable energy systems.

This paper proposes a Neural Network based hybrid computing model for wind speed prediction in renewable energy systems. Wind energy is one of the renewable energy sources which lower the cost of electricity production. Due to the fluctuation and nonlinearity of wind, the accurate wind speed prediction plays a major role in renewable energy systems. To increase the accuracy of wind speed prediction, a hybrid computing model is proposed. The proposed model is tested on real time wind data. The objective is to predict accurate wind speed based on proposed hybrid computing model which integrates Self Organizing feature Maps and Multilayer Perceptron network. The key advantages include higher accuracy, precision and minimal error. The results are computed by the training and testing methodologies. The experimental result shows that as compared to the conventional neural network models, the proposed hybrid model performs better in terms of minimization of errors.