In recent years, wind power generation is rapidly gaining popularity due to the major concerns about the excessive emissions and global energy crisis. In addition, this kind of power systems have shown more security options than others. Due to the highly variable and intermittent nature of the wind energy, it is crucial to achieve higher accuracy of long-term wind speed forecasts for improving ...

The intermittency of wind resources may appear as an important handicap for integrating large-scale wind power in existing grids. However, in Canada and the United States, studies show that coordination between interconnected power systems can be economically beneficial for both the wind power and electric utility industries if some power system management and market rules are applied. In order...

Utilization ofwind power as one of renewable resources of energy has been growing quickly all over the world in the last decades. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, assessment of the output power of this type of generators is always associated with some uncertainties. A precise wind power prediction can efficiently uphold transmission...

For accurate and efficient use of wind power, it is important to know the statistical characteristics, availability, diurnal variation, and prediction of wind speed. Prediction of wind power permits the scheduling of the connection or the disconnection of wind turbines to achieve optimal operating costs. In this paper, a simple and accurate method for predicting wind speed based on weather-sens...

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...

We discuss the statistical effects of predicting the power output of spatially distributed wind farms. Our forecasting procedure provides the expected power output for a time horizon up to 48 hours. It is based on the large scale wind field prediction which is generated operationally by the German weather service. In this paper we focus on the reduction of the forecast error for the aggregated ...

The power generated by a wind turbine largely depends on the wind speed. During time periods with identical hub-height wind speeds but different shapes to the wind profile, a turbine will produce different amounts of power. This variability may be induced by atmospheric stability, which affects profiles of mean wind speed, direction and turbulence across the rotor disk. Our letter examines turb...

This contribution presents the application of feed-forward neural networks to the problem of time series forecasting. This forecast technique is applied to the water flow and wind speed time series. The results obtained from the forecasting of these two renewable resources can be used to determine the power generation capacity of micro or mini-hydraulic plants, and wind parks, respectively. The...

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gra...