scholarly journals Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

2021 ◽  
Vol 7 ◽  
pp. e724
Author(s):  
Muhammad Ibnu Choldun Rachmatullah ◽  
Judhi Santoso ◽  
Kridanto Surendro
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Objective Function ◽  
Principal Component ◽  
Future Research ◽  
Wind Speed Prediction ◽  
The Neural Network ◽  
Speed Prediction ◽  
Hidden Layer ◽  
Hidden Neurons

Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.

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.

Wind Speed Prediction Based on Improved Grey Neural Network Model

2014 ◽  
Vol 548-549 ◽  
pp. 1235-1240
Author(s):  
Bin Zeng ◽  
Jian Xiao Zou ◽  
Kai Li ◽  
Xiao Shuai Xin
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Data Storage ◽  
Wind Farm ◽  
Grey System Theory ◽  
Grey System ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Wind speed forecasting is an effective method to improve power stability of wind farm. Grey system theory have certain advantages in the study of poor information and uncertainty problems, it is suitable for the system with limited computing power and data storage capacity, such as wind turbine control system. In order to further improve the prediction accuracy of grey model, we combined GM (1, 1) model and BP neural network prediction model in this paper, and improved the combined model by background value optimizing and introducing genetic algorithm. Through analyzing the simulation results and comparing the forecasting results with the actual wind speed, it is clear that the improved combined prediction model is superior to pure grey forecasting model and it meets the needs of the wind power control.

Sign in / Sign up

Export Citation Format

Share Document