基于因果聚类分析理论的维修成本控制体系研究

供电网络的维修维护工程比较复杂，影响的因素较多，影响方式多种多样，这给成本控制造成了很大困难。为了解决这个问题，我们采用数据分析，建立预测模型的方法，通过分析预测误差和预测的置信区间，确定维护成本的控制范围。为了提高预测得准确性，利用历史数据对要素变量进行因果聚类。然后将各类型中的变量进行主成份分析，降低变量集的维数。对选出的变量用神经网络算法构建预测模型，并利用其误差的性质确定误差范围，实现对成本的控制。

Research on the Maintenance Cost Control System of the Power Grid Enterprise

The maintenance of power supply network is more complex, there are so many influence factors that influence the cost of it in various ways, which caused the cost difficult to control. In order to solve this problem, the data analysis is used to establish the method of forecasting model, and the confidence interval of forecast error is made, which determine the range of the maintenance cost control. To improve the accuracy of the forecast, the historical data is used for causal clustering of the factor variables. Then, principal component analysis of the variables is conducted in each type of variable, and the dimension of the variable set is reduced. The neural network algorithm is used to construct the forecasting model and the error range is determined by the error property.