Abstract: In order to balance the forecast accuracy and computational efficiency, a wind power forecasting method for clustering wind turbines is proposed based on effective combination of Hopkins statistics (HS) and clustering methods, in which Hopkins Statistics is used to determine the clustering number of a wind farm, and wind turbines in a wind farm are clustered into several groups according to the identifying of similar characteristics by clustering method. Then power forecasting model of each clustering group is built separately, whose power output is added to obtain whole power output of the wind farm. In addition, the real-time monitoring wind speed, power output and their combination are taken as the inputs for clustered group model, and their influences on the accuracy of clustering forecast model are analyzed. The case analysis shows that the HS-Clustering based forecasting method can effectively forecast the output power of the whole wind farm with better accuracy and higher computational efficiency, wind speed is the main factor affecting clustering results, and wind power can be regarded as an important additional factor as to certain group models.