Wind speed forecasting is one of the most important technologies to guarantee the wind energy integrated into the whole power system smoothly. In this paper a hybrid model named EMD–ANN for wind speed prediction is proposed based on the Empirical Mode Decomposition (EMD) and the Artificial...

Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate forecasting is essential in all water resources applications. In this study, artificial neural network (ANN) and wavelet neural network (WNN) were utilized to forecast daily ET from temperature and wind speed...

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to...

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings,...

We review key aspects of forecasting using nonlinear models. Because economic models are typically misspecified, the resulting forecasts provide only an approximation to the best possible forecast. Although it is in principle possible to obtain superior approximations to the optimal forecast...

Wind power generated by wind turbines has a non-schedulable nature due to the stochastic nature of meteorological conditions. Hence, wind power predictions are required for few seconds to one week ahead in turbine control, load tracking, pre-load sharing, power system management and energy...

The aim of this study is to design a controller, based on model predictive control (MPC), to smooth the wind power output, which is generated from a wind farm, and subject to a variety of constraints on the system model. In order to employ the model predictive controller, we propose a wind power...

This article presents an adaptive very short-term wind power prediction scheme that uses an artificial neural network as predictor along with adaptive Bayesian learning and Gaussian process approximation. A set of recent wind speed measurements samples composes the predictor’s inputs. The...