ELM算法在用户用电行为分析中的应用  

Application of ELM Algorithm in the Analysis of Customer Electrical Behavior

作　　者：胡殿刚[1] 李韶瑜[1] 楼俏[2] 王琼[2] 程淼海 王国军[2] 李国辉[3,4] 

机构地区：[1]国网甘肃省电力公司,兰州730030 [2]国网甘肃省电力公司兰州供电公司,兰州730050 [3]福州大学数学与计算机科学学院,福州350116 [4]福建省网络计算与智能信息处理重点实验室,福州350116

出　　处：《计算机系统应用》2016年第8期155-161,共7页Computer Systems & Applications

基　　金：国家自然科学基金(61103175;61300104);教育部科学技术研究重点项目(212086);福建省科技创新平台建设(2009J1007);福建省自然科学基金(2013J01230);福建省高校杰出青年科学基金(JA12016);福建省高等学校新世纪优秀人才支持计划(JA13021)

摘　　要：对于非法用电行为的检测,电力企业通常采用传统的人工检查方式,而这种方式的准确率和效率往往都比较低.提出一种将极限学习机(ELM)应用于预测存在非法用电行为用户的方法.首先,在收集到的用户历史用电数据,对原始数据进行预处理.然后,应用ELM算法建立异常用电行为的神经网络模型.最后,在真实用电数据上进行实证分析,通过与随机森林算法建立的预测模型及预测结果的对比,证明提出的方法具有较高的准确率和较好的性能.In order to detect the illegal use of electricity, electrical enterprises generally adopt traditional manual examination ways. However, both of the accuracy and efficiency of the approaches are far from satisfaction. In this paper, an analysis method based on the Extreme Learning Machine（ELM） algorithm is proposed, which is used to predict the behavior of customers＇ illegal electric use. Firstly, it collects the historical electric usage data and preprocesses the data to make it suitable for analysis by the algorithms. Then, it applys an algorithm based on neural network model, which is called ELM, to build the model to describe the abnormal power utilization behavior of the customers. Finally, experiments on the real electrical consumption data are conducted to evaluate the proposed method. The experimental results demonstrate that the proposed method is accurate and efficient.

关 键 词：极限学习机 特征选择 用户用电行为 

分 类 号：F426.61[经济管理—产业经济] TP183[自动化与计算机技术—控制理论与控制工程]