Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks

Wind Speed Prediction Using Artificial Neural Networks

A multilayered artificial neural network has been used for predicting the mean monthly wind speed in regions of Cyprus where data are not available. Data for the period 1986-1996 have been used to train a neural network, whereas data for the year 1997 were used for validation. Both learning and prediction were performed with adequate accuracy. Two network architectures of the similar type have ...

Artificial Neural Networks for Wind Speed Prediction

Wind energy has become a main challenge of conventional relic fuel energy, chiefly with the flourishing operation of multi-megawatt sized wind turbines. Though, wind with sensible speed is not sufficiently sustainable all over to construct an inexpensive wind farm. The probable site has to be systematically investigated at least with respect to wind speed profile and air density. Modelling and ...

Wind Speed and Power Prediction Using Artificial Neural Networks

Short-term wind prediction over different time steps is vital for wind farms in operation for various applications. Considering the complexity of atmospheric processes governing wind, time series models are preferred over physical models for wind prediction. Artificial neural networks (ANNs), which perform a non-linear mapping between inputs and outputs, provide an alternative approach for wind...

Forecasting of Wind Speed Using Artificial Neural Networks

Wind speed forecast is essential in wind energy conversion system and may fail to operate power plant at non optimal region if not properly forecasted. This paper focuses the short term wind speed forecasting using conventional statistical method and artificial neural networks such as back propagation network (BPN), generalized regression neural network (GRNN) and radial basis function networks...

Prediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks

The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (G...

Prediction of Gas Lift Parameters Using Artificial Neural Networks