Techno Press
Techno Press

Wind and Structures   Volume 18, Number 6, June 2014, pages 619-631
DOI: http://dx.doi.org/10.12989/was.2014.18.6.619
 
New criteria to fix number of hidden neurons in multilayer perceptron networks for wind speed prediction
K. Gnana Sheela and S.N. Deepa

 
Abstract     [Full Text]
    This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.
 
Key Words
    hidden neurons; mean squared error; multilayer perceptron; neural networks; wind speed prediction
 
Address
K. Gnana Sheela and S.N. Deepa: Anna University, Regional Centre, Coimbatore, India
 

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