面向智能电网的大数据降维管理方案

Big Data Dimension Reduction Management Scheme for Smart Grid

针对智能电网数据繁多、维度较高、难以识别的技术问题,提出了降低大数据维度的构想,并设计出基于随机森林算法的物联网智能电网大数据管理系统。通过采用Bagging算法对数据样本训练、学习,建立起多个决策树构型,根据少数服从多数的投票法原则确定建立决策树的节点和分支,最终建立起成熟的随机森林算法模型,通过随机森林算法模型将智能电网中的大数据从高纬度降低到低纬度。本设计的方案大大减小了大数据处理难度,优化了数据处理的效率,增加了分析问题、解决问题的有效途径,为智能电网的健康、有序运行提供有力保障。

Aimed at the technical problems such as much data,high dimension,difficult to identify in smart grid data,the idea of reducing the big data dimension is proposed,and the big data management system of the Internet of Things smart grid based on random forest algorithm is designed. Multiple decision tree configurations are established by using the bagging algorithm to train and learn data samples,according to the minority majority voting principle,the nodes and branches of the decision tree are determined,and finally the mature random forest algorithm model is established,and the big data in the smart grid is reduced from high dimensionality to low dimensionality via the random forest algorithm model. The scheme designed in this paper greatly reduces the difficulty of big data processing,optimizes the efficiency of data processing,and increases the effective way of analyzing problems and solves problems,as well as providing powerful guarantee for the healthy and orderly operation of smart grid.