scholarly journals An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
V. Ranganayaki ◽  
S. N. Deepa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Wind Speed ◽  
Neural Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Ensemble Neural Network ◽  
Hidden Neurons ◽  
Selection Of

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

scholarly journals A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ummuhan Basaran Filik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Hybrid Approach ◽  
Mean Square ◽  
Least Mean Square ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Artificial Neural ◽  
Prediction Approach

A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS) algorithm and artificial neural network (ANN) method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE) and root mean square error (RMSE) are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

scholarly journals Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Energies ◽  
2017 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Athraa Ali Kadhem ◽  
Noor Wahab ◽  
Ishak Aris ◽  
Jasronita Jasni ◽  
Ahmed Abdalla
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction ◽  
Artificial Neural

scholarly journals Review on Methods to Fix Number of Hidden Neurons in Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
K. Gnana Sheela ◽  
S. N. Deepa
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Renewable Energy Systems ◽  
Wind Speed Prediction ◽  
Minimal Error ◽  
The Neural Network ◽  
Proposed Model ◽  
Speed Prediction ◽  
Hidden Neurons ◽  
Selection Of

This paper reviews methods to fix a number of hidden neurons in neural networks for the past 20 years. And it also proposes a new method to fix the hidden neurons in Elman networks for wind speed prediction in renewable energy systems. The random selection of a number of hidden neurons might cause either overfitting or underfitting problems. This paper proposes the solution of these problems. To fix hidden neurons, 101 various criteria are tested based on the statistical errors. The results show that proposed model improves the accuracy and minimal error. The perfect design of the neural network based on the selection criteria is substantiated using convergence theorem. To verify the effectiveness of the model, simulations were conducted on real-time wind data. The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction. The survey has been made for the fixation of hidden neurons in neural networks. The proposed model is simple, with minimal error, and efficient for fixation of hidden neurons in Elman networks.

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