In order to establish whether a particular site is suitable for a wind power project or not, investors, project engineers and system operators need to obtain an assessment. In so doing they must ensure that they take into account any uncertainties relating to meteorology and technology – something which will, in turn, have a direct effect on the funding of the project by the participating banks. The Karlsruhe weather service provider EWC is exploring new ways to minimise risks of this nature. In collaboration with the Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg (Center for Solar Energy and Hydrogen Research Baden-Württemberg, ZSW), an innovative method for obtaining long term correction of wind measurements (MCP) for wind power locations has been developed which significantly reduces the uncertainties contingent on weather and technology by comparison to the traditional processes.
Based on deep neural networks, this collaborative project on the part of the two partners from Southern Germany enables non-linear corrections to the long time series in order to improve correlation, that is, temporal concurrence with the measurements. The result is based on actual measured values on site and provides an hourly wind time series spanning a period of 34 years for the projected wind turbine generator and/or measurement site.
Conducting a detailed evaluation of the mechanical learning process, a clear superiority by comparison with traditional methods emerges.
Thus the frequency distribution of the wind speed as well as the correlation between measurement and long term data is optimised. In all instances under consideration, the method demonstrates significantly fewer errors in terms of yield estimation than is, for example, the case where classic processes using linear regression or the matrix method are deployed.