基于模糊信息粒化和最小二乘支持向量机的风电功率联合预测建模

A combined forecasting model for wind power predication based on fuzzy information granulation and least squares support vector machine

提出一种基于模糊信息粒化和最小二乘支持向量机的风电功率平均值预测和风电功率波动范围预测的联合预测模型建模方法。该方法首先对训练样本进行模糊信息粒化,根据需要提取各个窗口的有效分量信息,即各窗口的最小值、大致平均值和最大值。其次应用最小二乘支持向量机对各个分量分别建立预测模型,并使用自适应粒子群算法对各个分量模型进行优化。最后使用优化后的最小二乘支持向量机模型对风电功率平均值和风电功率波动范围进行联合预测。实例研究表明,该联合预测模型可以有效进行风电功率平均值预测和风电功率波动范围预测的联合预测,并能有效跟踪风电功率变化。

A combination prediction model modeling method for wind power average value prediction and wind power fluctuation range prediction is proposed, which is based on the fuzzy information granulation and least squares support vector machine （LSSVM）. Firstly, fuzzy information granulation of the training samples is made, and effective component information of each window is extracted according to the need, namely the minimum, average and maximum value of each window. Secondly, LSSVM of the prediction models are established for each component, and then the adaptive particle swarm algorithm is used to optimize each component model. Finally, the optimized LSSVM model is used for combined forecast in terms of wind power average value and wind power fluctuation range. The case study shows that the combined prediction model can effectviely predict wind power average value prediction and wind power fluctuation range, and accurately track the wind electric power change.