Wind energy today, has attracted widespread interest from among a variety of sources of renewable energy in the world. Owing to the increasing demand for production of electrical energy for electricity networks by using wind power, it is essential that wind power plants are actively incorporated in the network’s performance using an appropriate control system. In general, these wind power plant...

The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back P...

Determination of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we ...

Wind energy today, has attracted widespread interest from among a variety of sources of renewable energy in the world. Owing to the increasing demand for production of electrical energy for electricity networks by using wind power, it is essential that wind power plants are actively incorporated in the network’s performance using an appropriate control system. In general, these wind power p...

Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed t...

The intermittent nature of the wind creates significant uncertainty in the operation of power systems with increased wind power penetration. Considerable efforts have been made for the accurate prediction of the wind power using either statistical or physical models. In this paper, a method based on Artificial Neural Network (ANN) is proposed in order to improve the predictions of an existing n...

Short-term wind power forecasting is recognized today as a major requirement for a secure and economic integration of wind generation in power systems. This paper deals with the case of regional forecasting of wind power with a large number of wind farms involved. Due to the large amount of potentially available information and also because part of the wind farms may not be "observable", foreca...

This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnost...

We discuss the accuracy of the prediction of the aggregated power output of wind farms distributed over given regions. Our forecasting procedure provides the expected power output for a time horizon up to 48 hours ahead. It is based on the large scale wind field prediction which is generated operationally by the German weather service. Our investigation focuses on the statistical analysis of th...