一种基于电力大数据的反窃电预测方法
A prediction method of anti-electricity stealing based on big data of electric power
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摘要: 针对传统的反窃电预测方法准确度低的问题，提出了一种基于电力大数据的反窃电预测方法.该方法根据异常规则构造窃电数据样本，引入线损率增长率这一约束条件，使用4种机器学习分类算法分别在电压、电流和功率因数数据集上构建预测模型，将其输出的数据异常用户与线损异常用户相结合，输出疑似窃电用户清单.实验结果表明，该方法预测准确度令人满意，在疑似窃电用户识别方面是高效可行的.Abstract: Aiming at the problem of low accuracy of traditional prediction methods of anti-electricity stealing, a prediction method of anti-electricity stealing based on big data of electric power was proposed. This method firstly constructed samples of electricity stealing data according to abnormal rules, and introduced the growth rate of line loss rate constraint conditions.Then it used four machine learning classification algorithms to build a prediction model on the voltage, current and power factor data sets respectively, combined the users with abnormal data output and users with abnormal line loss to output a list of users suspected of stealing electricity.Experimental results showed that the prediction accuracy of the method was satisfactory,and it was efficient and feasible in identifying users suspected of stealing electricity.
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