基于神经网络的电力防盗系统研究与应用

Liu Yinghui, Wang Qingning, Zhang Donghui, Sun Xiangde, Shen Yang, Xu Xianglian
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引用次数: 3

摘要

目前,我国电力自动化管理的落后造成了大量的电能损失。为了改善这种情况,本文引入了一种防盗数学模型。首先,选取10个因素构建指标评价体系,利用数据挖掘技术对大量电力数据进行处理。然后,建立了基于BP神经网络的客户消费行为分析数学模型。利用该模型可以计算盗电嫌疑系数,并对电力消费者的信用等级进行分类。本文选取了一些典型的企业对反窃电模型进行了验证,得出了一个解决窃电问题的可行思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and Application of Electricity Anti-stealing System Based on Neural Network
Nowadays, the backwardness of the power automation management in our country causes the loss of a lot of energy. In order to improve the situation, an anti-stealing mathematical model is introduced in this paper. Firstly, ten factors are selected to build the indictor evaluation system, data mining is used to process lots of the electricity data. Then, a mathematical model based on BP neural network is built for analyzing the customer consumption behavior. With the model, the suspicion coefficient of electricity stealing can be calculated, and the credit rating of power consumer is also classified. In this paper, some typical companies are selected to verify the electricity anti-stealing model, and a conclusion that a feasible idea for the electricity stealing problem is drawn.
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