新能源用户参与度高的电力市场用户电量异常数据修正模型

Corrected Model of Abnormal Data of Electricity Consumption in Power Market with High Participation of New Energy User

为了精准识别电量异常数据并进行实时修正,保障用户用电安全,提出一种新能源用户参与度高的电力市场用户电量异常数据修正模型。以某含高比例新能源的省市为例,分析其电力市场用户电量可能存在的异常数据现象,搭建概率预测模型,通过离线训练及在线识别,结合阈值和电量预估区间,识别用户电量异常数据,按照用户有不完整负荷数据以及无负荷数据情况,将识别到用户电量异常数据分成两类,获取两种类型中需要修正的用户电量异常数据,将其作为输入,搭建基于深度神经网络的用户电量异常数据修正模型,输出用户电量异常数据修正结果。实验结果显示,该模型能够识别出用户电量异常数据并能精准识别用电数据异常时刻,并能够对识别出用电异常数据进行实时修正。

In order to accurately identify abnormal electricity data and make real-time correction to ensure the safety of users’electricity consumption,a corrected model of abnormal electricity data of users in the power market with high participation of new energy users is proposed.Taking a province and city with a high proportion of new energy as an example,this paper analyzes the possible abnormal data phenomenon of user electricity in its power market,builds a probability prediction model,identifies the abnormal data of user electricity through off-line training and online identification,combines with the threshold and electricity prediction interval,and divides the identified abnormal data of user electricity into two categories according to the incomplete load data and no load data of users.It then obtains the abnormal data of user electricity that need to be corrected in the two types.It is taken as input,a corrected model of abnormal data of user electricity is built based on deep neural network,and the network outputs the correction results of abnormal data of user electricity.The experimental results show that the model can identify the abnormal data of users’electricity consumption and accurately identify the abnormal time of electricity consumption data,and can correct the identified abnormal data of electricity consumption in real time.