[4] The problem gets more complex once the wind power starts providing more than a small percentage of the overall electricity supplied to the grid.

In the context of deregulation, more and more players appear on the market, thus breaking the traditional situation of vertically integrated utilities with quasi local monopolies.

An auction system permits to settle the electricity spot price for the various periods depending on the different bids.

Balancing of the 15-minute averaged power is required from all electrical producers and consumers connected to the grid, who for this purpose may be organised in sub-sets.

[citation needed] Note that the costs for positive and negative imbalances may be asymmetric, depending on the balancing market mechanism.

In general, wind power producers are penalized by such market system since a great part of their production may be subject to penalties.

They also serve as a basis for quantifying the reserve needs for compensating the eventual lacks of wind production.

Regarding the time axis, the forecast length of most of the operational models today is between 48 and 172 hours ahead, which is in adequacy with the requirements for the wind power application.

NWP models impose their temporal resolution to short-term wind power forecasting methods since they are used as a direct input.

The output consists of the expected instantaneous value of physical quantities at various vertical levels in a horizontal grid and stepping in time up to several hours after initiation.

Also, the initial conditions may contain errors (which in a worse case propagate), and the output is only available for discrete points in space (horizontal as well as vertical) and time.

As an example in the Netherlands, KNMI publishes 4 times per day expected values of wind speed, wind direction, temperature and pressure for the period the between 0 and 48 hours after initialization of the atmospheric model Hirlam with measured data, and then the period before forecast delivery is of 4 hours.

Since wind farms are not situated on these nodes, it is then needed to extrapolate these forecasts at the desired location and at turbine hub height.

Knowledge of all relevant processes is therefore crucial when developing a purely physical prediction method (such as the early versions of the Danish Prediktor).

The physical phenomena are not decomposed and accounted for, even if expertise of the problem is crucial for choosing the right meteorological variables and designing suitable models.

Model parameters are estimated from a set of past available data, and they are regularly updated during online operation by accounting for any newly available information (i.e. meteorological forecasts and power measurements).

Today, major developments of statistical approaches to wind power prediction concentrate on the use of multiple meteorological forecasts (from different meteorological offices) as input and forecast combination, as well as on the optimal use of spatially distributed measurement data for prediction error correction, or alternatively for issuing warnings on potentially large uncertainty.

[10] Predictions of wind power output are traditionally provided in the form of point forecasts, i.e. a single value for each look-ahead time, which corresponds to the expectation or most-likely outcome.

Today, a major part of the research efforts on wind power forecasting still focuses on point prediction only, with the aim of assimilating more and more observations in the models or refining the resolution of physical models for better representing wind fields at the very local scale for instance.

Therefore, in complement to point forecasts of wind generation for the coming hours or days, of major importance is to provide means for assessing online the accuracy of these predictions.

In practice today, uncertainty is expressed in the form of probabilistic forecasts or with risk indices provided along with the traditional point predictions.

It has been shown that some decisions related to wind power management and trading are more optimal when accounting for prediction uncertainty.

For the example of the trading application, studies have shown that reliable estimation of prediction uncertainty allows wind power producer to significantly increase their income in comparison to the sole use of an advanced point forecasting method.

[10] The correlation between wind output and prediction can be relatively high, with an average uncorrected error of 8.8% in Germany over a two-year period.