基于差分进化和规则约简的二型模糊方法在风电预测中的应用

当前,风力发电量占比不断增加,对风电预测的要求越来越高,但由于风能本身存在的间歇性和不确定性等问题,风电预测精度并不能达到理想的效果.为了降低预测模型复杂性,并提高风电预测精准度,本文提出了一种基于差分进化和规则约简的二型模糊方法.该模型给出了一种剪枝策略进行二型模糊规则的约简,在此基础上,采用差分进化算法进行二型模糊系统全部参数的优化学习.最后,通过与一型模糊方法和支持向量回归方法进行了对比,证明了文中所提出的模型具有更好的预测精度.

Application of Type-2 Fuzzy Method Based on Differential Evolution and Rule Reduction in Wind Power Prediction

Nowadays, with a higher proportion of wind power generation, wind power prediction is increasingly demanding. It is, however, not satisfyingly accurate due to the intermittent and uncertain nature of wind energy. In order to reduce the complexity of the prediction model and improve accuracy, we propose a type-2 fuzzy system based on differential evolution and rule reduction in this study. This model provides a pruning strategy for the reduction in type-2 fuzzy rules, based on which and the differential evolution algorithm is adopted to optimize the parameters of the reduced type-2 fuzzy system. Finally, the prediction accuracy of our method is higher than that of the type-1 fuzzy system and the support vector regressor, according to comparison.