The importance of wind in smart grids with a large number of renewable energy resources is increasing. With the growing infrastructure of wind turbines and the availability of time-series data with high spatial and temporal resolution, the application of data mining techniques comes into play and offers the chance for successfully mining precise knowledge for prediction, planning, and optimization. With efficient and specialized data mining techniques that extract knowledge from large wind data bases, a stable integration of wind energy into smart grids becomes possible.

WindML aims at minimizing the obstacles for data-driven research in the wind power domain. Before the practitioner can start the data mining process, numerous steps have to be taken like accessing different data bases via interfaces, preprocessing, choice and parameterizations and appropriate methods. With a framework that bounds specialized mining algorithms to data sets of a particular domain, frequent steps of the data mining process chain can be re-used and simplified.