Add to ORKGThis paper presents the experimental results and analysis of artificial neural network (ANN) models to forecast wind speed for wind turbine generation. A modified cascade correlated (MCC) training algorithm was developed for forecasting wind speeds and its performance is compared with those of the existing well established back propagation with momentum (BPM) and back propagaion with Bayesian regularization (BR) training algorithms. Results are analysed in the standardized methodology of prediction accuracy to have a clear idea about the model skills. It shows that MCC model performs better with respect to the BPM and BR for the wind speed forecasting in this event of three hourly prediction spheres
Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess-ment,...
The need to deliver accurate predictions of renewable energy generation has long been recognized by ...
We have investigated the feasibility of using neural networks to make predictions of long term energ...
Predicting short term wind speed is essential in order to prevent systems in-action from the effects...
Abstract- Wind speed forecasting is an essential prerequisite for the planning, operation, and maint...
In this paper, an Artificial Neural Network (ANN) methodology to cast super-short term (under 30 sec...
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and arti...
The assessment of the suitability of a wind system depends largely on the prediction of the wind pot...
Renewable energies such as wind power have become integral parts of modern power networks. Short-ter...
To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is c...
AbstractThis paper presents an intelligent computing model for wind speed prediction, which uses bac...
Wind speed forecasting is critical for wind energy conversion systems since it greatly influences th...
Wind speed-time series data typically exhibit autocorrelation, which can be defined as the degree o...
In this paper a time series prediction of wind speed with artificial neural networks is presented. ...
Prediction is one of the most important techniques in determining the resulting wind speed. The deci...
Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess-ment,...
The need to deliver accurate predictions of renewable energy generation has long been recognized by ...
We have investigated the feasibility of using neural networks to make predictions of long term energ...
Predicting short term wind speed is essential in order to prevent systems in-action from the effects...
Abstract- Wind speed forecasting is an essential prerequisite for the planning, operation, and maint...
In this paper, an Artificial Neural Network (ANN) methodology to cast super-short term (under 30 sec...
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and arti...
The assessment of the suitability of a wind system depends largely on the prediction of the wind pot...
Renewable energies such as wind power have become integral parts of modern power networks. Short-ter...
To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is c...
AbstractThis paper presents an intelligent computing model for wind speed prediction, which uses bac...
Wind speed forecasting is critical for wind energy conversion systems since it greatly influences th...
Wind speed-time series data typically exhibit autocorrelation, which can be defined as the degree o...
In this paper a time series prediction of wind speed with artificial neural networks is presented. ...
Prediction is one of the most important techniques in determining the resulting wind speed. The deci...
Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess-ment,...
The need to deliver accurate predictions of renewable energy generation has long been recognized by ...
We have investigated the feasibility of using neural networks to make predictions of long term energ...