基于改进随机森林的电力用户欠费风险分析预警

针对当前电网企业电费回收风险,提出了一种基于改进随机森林的电力用户欠费风险分析预警方法。首先,针对欠费用户、正常缴费用户的类别分布不均衡问题,采用SMOTE算法优化原始用户样本分布；接着,选择信息值计算各属性与目标类别属性的相关性,进而优化节点属性的选择；然后,针对影响随机森林分类准确率和性能的主要参数：树的规模nTree、叶子节点的最小样本数minLeaf和属性子集的数量K,采用加温模拟退火算法搜寻最优参数组合；最后,采用改进的随机森林算法对用户未来是否欠费进行分析预测,得到潜在欠费高风险用户。将该方法与逻辑回归、决策树等常用分类算法进行了对比分析,结果验证了该方法的有效性。#$NL关键词：电力用户；欠费风险预测；随机森林算法；SMOTE；信息值；参数组合；加温模拟退火算法

Risk Analysis and Early Warning of electricity customers

Aiming at the power grid company charging risk, a risk analysis and early warning based on optimized random forest algorithm is proposed. Firstly, the SMOTE algorithm is used to optimize the user sample set distribution to solve the inhomogeneous distribution of the arrearage users and normal users. Secondly, selecte the Information Value to calculate the correlation between attribute features and target category, and then the random selection of node attributes is optimized. Thirdly, for the main parameters that affect the accuracy and performance of the random forest algorithm: tree size nTree, minimum sample leaf nodes minLeaf and attribute subset size K, it use the simulated annealing algorithm to obtain the best combination. Then, the optimized random forest algorithm is used to predict the future arrears of users, and obtains the potential high risk users. The method is compared with other classification algorithms such as logistic regression and decision tree, and the experimental results show that the method is effective.#$NLKeywords: electricity customers; arrears risk forecast ; random forest; SMOTE; Inofrmation Value; parameter combination; heating simulated annealing