基于采样技术和LightGBM的用户用电异常检测模型

Anomaly Detection Model of Consumer Power Consumption Based on Sampling Technology and LightGBM

在大数据的时代背景下,我国电力事业信息化的发展日趋重要,尤其是需要使用计算机技术对用电数据进行分析.对于用户用电异常的分析问题,传统方法既耗时又耗力,这就需要引入机器学习的相关方法自动的识别异常信息.现阶段,用电异常分析主要基于传统的异常检测算法或深度神经网络,传统异常检测算法运行精度不足而深度神经网络计算速度又过慢.针对目前存在的不足,本分采用了基于采样技术和LightGBM的用户用电异常检测模型,把用电异常检测问题看作分类问题,并使用当前流行的分类模型LightGBM进行训练,在保证速度快的前提下提高了检测的准确率.

In the context of big data,the informatization of China’s power industry is becoming more important,especially the analysis of power consumption data with computer technology.For the analysis of abnormal user power consumption,traditional methods are time-consuming and labor-intensive.This requires the introduction of machine learning related methods to automatically identify anomaly information.At this stage,the analysis of abnormal power consumption is mainly based on traditional anomaly detection algorithms or deep neural networks.Anomaly detection algorithms have insufficient accuracy and calculations with deep neural networks are quite slow.In response to the current shortcomings,this study adopts an anomaly detection model of user power consumption based on sampling technology and LightGBM.The detection of abnormal power consumption is regarded as a classification problem,and the popular classification model LightGBM is applied to training.The detection accuracy is improved while fast speed is maintained.