摘要：

对于用户异常用电行为的检测,电力企业通常采用人工检查的方法,但该方法需要消耗大量的人力、物力,且容易受主观因素的影响。为此,提出一种基于岭回归模型的异常用电行为识别算法。通过收集用户用电数据,对岭回归模型进行训练,并将训练好的模型用于异常用电行为的自动检测。为捕获未知的用户用电行为类别样本信息,在岭回归模型的基础上引入图正则项。考虑到用电数据的非线性分布特性,通过核函数的方式,将原始数据映射到高维希尔伯特空间,得到基于图正则的非线性岭回归模型。实验结果表明,与最小二乘、岭回归、图正则岭回归模型相比,该算法具有更高的识别准确率。

关键词: 用电行为分析, 岭回归, 图正则, 非线性分布, 半监督学习

Abstract:

For the detection of abnormal electricity behavior by users,power companies usually adopt manual inspection methods,however,this method requires a lot of manpower and material resources,and is influened by subjective factors.Therefore,an abnormal electricity behavior recognition algorithm based on ridge regression model is proposed.By collecting user electric data,the model is trained and the trained model is used for automatic detection of abnormal electricity behavior.In order to capture the sample information of unknown users’ electricity behavior categories,the graph regularization term is introduced on the basis of the ridge regression model.Taking into account the non-linear distribution characteristics of electricity data,the original data is mapped to high-dimensional Hilbert spaces through kernel functions,and a nonlinear ridge regression model based on graph regularity is gained.Experimental results show that compared with least squares,ridge regression,and graph regularization ridge regression models,this algorithm has higher recognition rate.

Key words: electricity behavior analysis, ridge regression, graph regularization, nonlinear distribution, semi-supervised learning

中图分类号: 

• TP391