STRAIGHT

In order to achieve the expansion targets for wind energy set by new government, it is necessary to develop a large number of wind farms in a short time. The basis for wind farm planning at a new site is the estimation of the expected energy yields and the selection of suitable wind turbines.
At present, the yield estimation is subject to high uncertainties. In addition, it is time-consuming and costintensive, especially due to the currently required one-year wind measurements. The aim of the project is therefore to enable qualitatively better yield estimates in a shorter time and at significantly lower costs through improvements along the entire process chain. To achieve this goal, procedures are being developed that provide a better data basis (e.g. reanalyses, roughness data) for the wind sector. In addition, innovative procedures from the field of data science such as machine learning or model ensembles are used at various points to enable an accurate estimation of energy yields in a shorter time.
The merging of the various methods and data into an overall process enables a high degree of automation of yield assessments in addition to the increase in quality. Ultimately, the project thus creates the basis for a reduction in project risks for planners and project developers. In addition, the developed methods can also be used for more precise regional potential assessments and thus contribute to better planning of wind energy expansion.
The University of Kassel is focusing its research on the development of procedures for the long-term correction of short-term wind measurements using artificial intelligence methods and the improved estimation of design wind conditions.