基于高斯混合模型的智能电表误差数据挖掘与分析方法

Data mining and analysis method for smart meter error data based on Gaussian mixture model

为了合理科学的选择误差最小的智能电表供给用电客户,设计了基于高斯混合模型的智能电表误差数据挖掘与分析方法。首先,分析了高斯混合模型与EM算法的基本思路,其次对智能电表误差数据求3次标准差作为建模数据,建立了以高斯混合算法为基础的智能电表误差数据模型,最后与传统的K-means聚类算法模型进行对比测试。实验结果表明,相对于其他聚类算法,所设计的方法轮廓系数值更大,性能更优。能够用于在大量的数据中寻找误差最小的智能电表,并能够给智能电表厂家反馈产品意见,同时还具有产品市场划分等功能。

In order to reasonably and scientifically select the smart meter with the smallest error to supply electricity to customers,a data mining and analysis method for smart meter error based on Gaussian mixture model is designed.First,the basic ideas of Gaussian mixture model and EM algorithm are analyzed.Secondly,the standard deviation of the smart meter error data is calculated as the modeling data,and the error data model of the smart meter based on the Gaussian mixture algorithm is established.Finally,it is combined with the traditional K-means.Class algorithm model for comparison test.The experimental results show that compared with other clustering algorithms,the designed method has a larger contour coefficient value and better performance.It can be used to find the smart meter with the smallest error in a large amount of data,and it can give feedback to the smart meter manufacturer on the product,and it also has functions such as product market division.