Connecting wind power to the power grid has recently become more common. To better manage and use wind power, its strength must be predicted precisely, which is of great safety and economic significance. In this paper, the short-term power prediction of wind power is based on self-adaptive niche particle swarm optimization (NPSO) in a neural net. Improved PSO adopts the rules of classification ...

In this paper different prediction models based on methods of the artificial intelligence are studied for wind power prediction of single wind farms. The used methods are neural networks, mixture of experts, support vector machines and nearest neighbour search with a superior particle swarm optimization. We build for day-ahead prediction and for short-term prediction with a prediction horizon o...

This paper describes different methods to estimate the uncertainty of wind power forecasts in terms of prediction intervals. The single methods and an ensemble average model have been applied to shortest-term wind power forecasts (forecast horizon = 1, 2, 4 & 8 h) of 62 spatially distributed wind farms in Germany to obtain intervals with a nominal reliability of 90, 95 and 98 %. Furthermore the...

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...

Due to the intermittency of wind power generation, it is very hard to manage its system operation and planning. In order to incorporate higher wind power penetrations into power systems that maintain secure and economic power system operation, an accurate and efficient estimation of wind power outputs is needed. In this paper, we propose the stochastic prediction of wind generating resources us...

A clustering approach is presented for short-term prediction of power produced by a wind turbine at low wind speeds. Increased prediction accuracy of wind power to be produced at future time periods is often bounded by the prediction model complexity and computational time involved. In this paper, a trade-off between the two conflicting objectives is addressed. First, a set of the most relevant...

Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle...

This paper investigates the performance of two simple wind power prediction models, an autoregressive one with exogenous input (ARX-model) and a neural network based one, none of which employs weather prediction data. The models are applied for predicting wind power production in three different wind parks, for which data are available. The error of the models is investigated for various foreca...