基于时间序列建模的电力营销客户交易行为分析

针对电力系统对客户服务的效率低、针对性差的问题,提出了一种采用云模型、离散余弦变换和反向传递神经网络的客户分类算法.通过云模型提取价格敏感度、投诉率、欠费率、销售变现天数以及忠诚度的变化波动情况的云测度,结合余弦变换计算得到特征向量,输入反向传递神经网络训练,从而得到自动客户分级识别的模型.结果表明,提出的模型可以有效实现客户分级以及销售状况评估,其平均检测精确度可达95%以上.

Transaction behavior analysis for power marketing customer based on time series modeling

In view of low efficiency and poor pertinence of power system for customer service, a customer classification algorithm based on cloud model, discrete cosine transformation and reverse transfer neural network was proposed. The cloud measure of fluctuating situation in price sensitivity, complaint rate, arrears rate, sales cash-in days and loyalty were extracted by a cloud model; eigenvectors were calculated and obtained by cosine transformation; the reverse transfer neural network was input and a model for automatic customer classification and recognition was obtained. The results show that the as-proposed model can effectively achieve customer classification and sales evaluation, and its average detection accuracy can reach more than 95%.