基于改进模糊c均值聚类中心优化算法的负荷分类方法

Load Classification Method Based on Improved Fuzzy C-means Clustering Center Optimization Algorithm

为解决当前负荷预测模型中聚类中心随机性大、聚类结果质量差、稳定性差的问题,将传统的模糊c均值聚类和局部密度法结合,提出基于改进模糊c均值聚类中心优化算法的负荷分类方法。首先,对收集得的负荷数据进行归一化处理,利用局部密度公式选择初始聚类中心;接着,对日负荷聚类,以某电网典型负荷作为算例验证该算法。结果表明,该算法有较好的鲁棒性,提高了负荷数据聚类的有效性。

In order to solve the problems of high randomness,poor quality and poor stability of the clustering center in the current load forecasting model,the load classification method is based on improved fuzzy C-means clustering center optimization algorithm,proposed by combining the traditional fuzzy C-means clustering method with local density method.Firstly,the collected load data are normalized,and the initial cluster center is selected by local density formula.Then,a typical user load of a power grid is taken as an example to verify the proposed algorithm.The results show that the proposed algorithm has good robustness and improves the validity of load data clustering.