基于模糊C均值聚类算法的用电行为模式分类

Power consumption behavior pattern classification based on fuzzy C-mean clustering algorithm

中文关键词: 用电模式  聚类分析  最短距离法  改进模糊 C均值

英文关键词: electricity consumption patterns  cluster analysis  shortest distance method  fuzzy C-means

中文摘要:

      通过对负荷数据的归类分析，可以得到电力用户的用电行为特征，为需求响应策略制定和效果评估提供支撑。首先，对负荷数据进行预处理，包括非正常数识别与处理，以及平滑处理去除毛刺数据；其次，针对模糊C均值聚类算法对初始聚类中心敏感、易陷入局部最优和受噪声影响大等问题，提出采用最短距离法聚类为模糊 C 聚类提供初始聚类中心、利用有效性分析类内样本相似程度和不同类之间独立程度来判别聚类结果优劣、以及通过数据密度识别并剔除噪声点等改进措施，提升了模糊 C 均值聚类算法性能；最后，通过对比其他方法以及对某纺织企业负荷聚类分析，验证了改进算法的正确性与有效性。

英文摘要:

      Through the classification and analysis of load data, the behavior characteristics of power users can be obtained,which provides support for the formulation of demand response strategies and effect evaluation. Firstly, the load data is preprocessed including identifying and processing abnormal numbers,and smoothing to remove burr data. Secondly, with the reason that the fuzzy C-means clustering algorithm is sensitive to the initial cluster center, easy to fall into local optimality, and is greatly affect-ed by noise, the shortest distance clustering method is proposed to provide the initial clustering center for fuzzy C clustering. The efficiency index is used to select the best clustering results of different categories, and the data density is used to identify and eliminate noise points.Finally, through comparison with other methods and cluster analysis of a certain hemp spinning enterprise’s load, the correctness and effectiveness of the improved algorithm is verified.