The dynamic development of wind power in recent years has generated the
demand for production forecasting tools in wind farms. The data obtained
from mathematical models is useful both for wind farm owners and
distribution and transmission system operators. The predictions of
production allow the wind farm operator to control the operation of the
turbine in real time or plan future repairs and maintenance work in the
long run. In turn, the results of the forecasting model allow the
transmission system operator to plan the operation of the power system
and to decide whether to reduce the load of conventional power plants or
to start the reserve units.
The presented article is a review of the currently applied methods of
wind power generation forecasting. Due to the nature of the input data,
physical and statistical methods are distinguished. The physical
approach is based on the use of data related to atmospheric conditions,
terrain, and wind farm characteristics. It is usually based on numerical
weather prediction models (NWP). In turn, the statistical approach uses
historical data sets to determine the dependence of output variables on
input parameters. However, the most favorable, from the point of view of
the quality of the results, are models that use hybrid approaches.
Determining the best model turns out to be a complicated task, because
its usefulness depends on many factors. The applied model may be highly
accurate under given conditions, but it may be completely unsuitable for
another wind farm.