Objectives:

Short-term prediction is used operationally in a number of countries. The number one complaint by the utilities that use the predictions operationally is that the predictions are not of the same quality as they are used to from load predictions. The short-term prediction models themselves (defined as the "translator" of the NWP model results into wind power forecasts) are quite accurate if fed with measured data. The models also reach a significantly better result than pure meteorological forecasts. However, the main error source is the NWP model. WP-4 tries to especially help out in cases of bad predictions, where the performance of the NWP model for a particular site is not good enough.

The reasons for this less-than-adequate performance can be insufficient resolution of the local flows, not good enough parameterisation of physical phenomena on a scale below the grid resolution, a non-representative data source feeding the model in the vicinity of the site, and a number of other (often large-scale) effects. The main push in this WP is to use meso-scale and CFD models to capture the sub-scale effects not resolved by the main NWP model. The main idea here is that better forecasts of the worst forecasted sites leads to a higher quality forecast for the areas, thus increasing the usability of wind power in a large-scale grid and reducing the "ecological rucksack" of wind power.

Description of Work:

The aim of this work package is to improve existing physical models or to elaborate more advanced ones for wind resource prediction especially in complex terrain. The developed models will consider as input high-resolution meteorological information. Due to the shortening of the modelled area, more detailed models for the terrain will be developed. The Work Package will examine the benefits from downscaling wind power using advanced CFD modelling.On the other hand, multi-dimensional MOS correction matrix will be generated based on a number of simulations with KAMM. This will increase the speed of a physical model and will account for Mesoscale effects.Finally, the long-term (up to 7 days ahead) wind resource predictability will be assessed.

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