  Wind power forecasting (WPF) is significant to guide the dispatching of grid
and the production planning of wind farm effectively. The intermittency and
volatility of wind leading to the diversity of the training samples have a
major impact on the forecasting accuracy. In this paper, to deal with the
training samples dynamics and improve the forecasting accuracy, a data mining
approach consisting of K-means clustering and bagging neural network is
proposed for short-term WPF. Based on the similarity among historical days,
K-means clustering is used to classify the samples into several categories,
which contain the information of meteorological conditions and historical power
data. In order to overcome the over fitting and instability problems of
conventional networks, a bagging-based ensemble approach is integrated into the
back propagation neural network. To confirm the effectiveness, the proposed
data mining approach is examined on real wind generation data traces. The
simulation results show that it can obtain better forecasting accuracy than
other baseline and existed short-term WPF approaches.
