scholarly journals An Intelligent Computing Model for Wind Speed Prediction in Renewable Energy Systems

2012 ◽  
Vol 30 ◽  
pp. 380-385 ◽  
Author(s):  
Gnana Sheela K ◽  
S.N. Deepa
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Energy Systems ◽  
Renewable Energy Systems ◽  
Intelligent Computing ◽  
Wind Speed Prediction ◽  
Computing Model ◽  
Speed Prediction

Application of the Maximum Entropy Method for Determining a Sensitive Distribution in the Renewable Energy Systems

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Gholamhossein Yari ◽  
Zahra Amini Farsani
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Maximum Entropy ◽  
Energy Systems ◽  
Rayleigh Distribution ◽  
Entropy Method ◽  
Climatic Data ◽  
Data Set ◽  
Renewable Energy Systems

In the field of the wind energy conversion, a precise determination of the probability distribution of wind speed guarantees an efficient use of the wind energy and enhances the position of wind energy against other forms of energy. The present study thus proposes utilizing an accurate numerical-probabilistic algorithm which is the combination of the Newton’s technique and the maximum entropy (ME) method to determine an important distribution in the renewable energy systems, namely the hyper Rayleigh distribution (HRD) which belongs to the family of Weibull distribution. The HRD is mainly used to model the wind speed and the variations of the solar irradiance level with a negligible error. The purpose of this research is to find the unique solution to an optimization problem which occurs when maximizing Shannon’s entropy. To confirm the accuracy and efficiency of our algorithm, we used the long-term data for the average daily wind speed in Toyokawa for 12 yr to examine the Rayleigh distribution (RD). This data set was obtained from the National Climatic Data Center (NCDC) in Japan. It seems that the RD is more closely fitted to the data. In addition, we presented different simulation studies to check the reliability of the proposed algorithm.

New SVM kernel soft computing models for wind speed prediction in renewable energy applications

2019 ◽  
Vol 24 (15) ◽  
pp. 11441-11458 ◽  
Author(s):  
Yogambal Jayalakshmi Natarajan ◽  
Deepa Subramaniam Nachimuthu
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Soft Computing ◽  
Energy Applications ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Computing Models

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.

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.

THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

2020 ◽  
Vol 2020 (57) ◽  
pp. 35-41
Author(s):  
K.S. Klen ◽  
◽  
M.K. Yaremenko ◽  
V.Ya. Zhuykov ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Error ◽  
Average Energy ◽  
Interpolation Formula ◽  
Wind Speed Prediction ◽  
Wind Generator ◽  
Prediction Horizon ◽  
Speed Prediction ◽  
Using Data ◽  
Predictor Corrector

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

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