Karlsruhe/Stuttgart, 28/11/2013. 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 (Centre 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. It emerged from the study that, by the time a measuring period of nine months has elapsed, the process put forward by the south German research scientists and service providers will attain the level of quality achieved by the classic methods. Where lengthy observation time series are concerned, the full strength of the process is brought to bear: even at complex locations, previous errors can be reduced by up to 50% where the duration of measurement exceeds 12 months.
