基于PCA和决策树模型的异常电费数据检测和识别研究

Research on the Detection and Identification of Abnormal Electricity Bill Data Based on PCA and Decision Tree Model

为解决电力公司当前主要依赖主观经验人工验证和判断异常用电数据分析方法准确率低下,同时耗费大量的人力物力,效率低下的问题,本文基于某公司的电力营销大数据并结合外部天气、政策等因素的影响,利用机器学习等算法对异常的用电数据进行检测和识别。首先对数据集进行清洗和预处理,对数据中的错误数据、空缺数据、不一致数据进行处理并转换成标准的可接受的处理格式;接着利用主成分分析法对数据进行简化、降维处理,提取出隐藏在数据间的重要特征;最后,利用决策树算法,对预处理后的数据进行训练和测试。检测的结果表明,本文提出的算法模型能够有效地提升拦截准确率、降低漏报率和误报率。该方法可应用于各供电企业,提高人工审核的效率,降低企业的经济损失,从而不断提高相关供电企业的服务水平。

In order to solve the problem that power company mainly relies on subjective experience for manual verification and judgment of abnormal power consumption data analysis method,the accuracy rate is low,and it consumes a lot of manpower and material resources,and the efficiency is low.Based on the power marketing big data of a company and the influence of external weather,policies and other factors,this paper uses algorithms such as machine learning to detect and identify abnormal power consumption data.In this paper,the data set is first cleaned and preprocessed,and the wrong data,vacant data,and inconsistent data in the data are processed and converted into a standard acceptable processing format;then the principal component analysis method is used to simplify and reduce the data.Dimensional processing is used to extract important features hidden in the data;finally,the decision tree algorithm is used to train and test the preprocessed data.The detection results show that the algorithm model proposed in this paper can effectively improve the interception accuracy,reduce the false negative rate and the false positive rate.The method can be applied to each power supply enterprise to improve the efficiency of manual auditing,reduce the economic loss of the enterprise,and continuously improve the service level of the relevant power supply enterprise.