The instability of wind power has a serious impact on the power grid system, and an accurate wind power forecasting is greatly demanded in practice. Due to the time-varying nature, the distribution of weather variables such as wind speed, wind direction and temperature and/or the functional relationship of the power output relating to these weather variables are likely to change over time, which are often known as “concept drifts”. However, most of the prediction models usually fail to adapt to such concept drifts. This would deteriorate the prediction accuracy, especially for the short-term prediction with high accuracy requirements. Motivated by this, this paper proposes an adaptive short-term wind power prediction method, aimed at automatically detecting the occurrence of concept drifts and updating the forecast model accordingly. The proposed method consists of three main steps, extract sample-related and sequence-related features of the weather data, cluster the data based on the similarity of the features extracted, and develop a new power prediction model with automatic drift detection and adaptation for each cluster. The comparison results show that the prediction performance of the proposed method is superior to that of the competitive methods.