Selection of number of hidden neurons in neural networks in renewable energy systems

Abstract

This paper presents a new approach to select number of hidden neurons in neural network in renewable energy systems. The random selection of number of hidden neurons might cause over fitting and under fitting problems in neural networks. The proper selection of neurons in hidden layer is important in the design of neural network model. To fix hidden neurons, 91 various criteria are examined based on estimated mean squared error. The convergence analysis is performed for the various proposed criteria. To verify the effectiveness of the proposed model, simulations were conducted on real time wind data. Results show that with minimum error the proposed approach can be used in renewable energy systems.