Abstract：It is significant to predict the change trend and change space of the wind power accurately for wind farm and the power system stable operation as wind power has the characteristics of volatility, intermittent and randomness. This paper conducted the time series of daily active power with fuzzy information granulation, and did the regression forecasting of the data after granulation with the support vector machine(SVM) prediction model, then simulated and tested this algorithm using the data from the wind farm located in the Gansu Jiu Quan wind farm of China. The results show that the new method has better prediction about the change trend and change space of the future wind power, and prove the feasibility and effectiveness of the model.
陈伟, 赵庆堂, 赵锦苹. 基于信息粒化和支持向量机的风电场功率变化趋势和变化空间的预测算法[J]. 电工技术学报, 2013, 28(1增): 169-173.
Chen Wei, Zhao Qingtang, Zhao Jinping. Combined Model Based on the Information Granulation and SVM for Predicting Change Trend and Change Space of Wind Power. Transactions of China Electrotechnical Society, 2013, 28(1增): 169-173.
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2013, Vol. 28 
