摘要
长期以来窃电问题一直困扰着电力企业,它不仅损害了供电企业的合法权益,扰乱了正常的供用电秩序,而且给安全用电带来了威胁.通过机器学习算法,对电力用电数据进行分析处理,可以预测用户是否存在窃电行为.基于电力数据中用户用电量提取相关特征,结合随机森林算法,提出了一种预测用户是否具有窃电行为的方法.对比多组实验数据,调节特征数量以及算法参数,以提高预测准确率和预测速度.
For a long time, the problem of electricity stealing has been plaguing power enterprises. It not only detriments the legitimate rights and interests of power enterprises, disturbs the normal order of the power supply, but also causes the electrical safety threat. The data of electrical power with machine learning algorithms is analyzed, which can predict the existence of users stealing power behavior. Based on feature extraction of electricity consumption in power data, and by using the random forests algorithm, a method of predicting the existence of users stealing power behavior is proposed. By comparing multiple sets of experimental data, the parameters of the algorithm are adjusted to improve the accuracy of forecasting.
出处
《上海电力学院学报》
CAS
2017年第4期331-336,共6页
Journal of Shanghai University of Electric Power
基金
国家自然科学基金(61403247)
上海市信息安全综合管理技术研究重点实验室开放课题(AGK2015005)
上海市科学技术委员会地方能力建设项目(15110500700)
关键词
随机森林
分类
窃电用户
机器学习
random forests
classify
stealing users
machine learning
