For management and trading purposes, information on short-term wind generation (from few hours
to few days ahead) is even more crucial at large offshore wind farms, since they concentrate a large
capacity at a single location. The most complete information that can be provided today consists of
probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble
predictions as input. The paper concentrates on the test case of the Horns Rev wind farm over a period
of approximately one year, in order to describe, apply and discuss a complete ensemble-based forecasting
methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power
through a suitable power curve model. The relevance and benefits of employing a newly developed orthogonal
fitting method for the power curve model over the traditional least-squares one are discussed.
The obtained ensemble forecasts of wind power are then converted into predictive distributions with
an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance
model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained
predictive distributions. Such a methodology has the benefit of yielding predictive distributions
that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts,
while taking advantage of their high resolution.
|Series||D T U Compute. Technical Report|