基于欠完备自编码器的用户用电行为分类分析方法

Classification analysis method for electricity consumption behavior based on undercomplete autoencoder

针对电力大数据背景下用户用电行为复杂多变、分析困难的问题,提出了一种基于欠完备自编码器的用户用电行为分类分析方法。首先,通过欠完备自编码器对智能电表数据进行编码,实现对原始数据的特征抽取,并使用反向传播(BP)神经网络进行用户用电行为分类分析;然后,对最佳编码比率进行优选,并结合用户的典型用电特征作为神经网络的输入,提高了分类准确率;最后,在爱尔兰智能电表数据集上进行了仿真实验,并与直接使用BP神经网络进行对比,分析表明,文中所提出的用户用电行为分类分析方法不仅可以提高检测准确率,帮助电力公司更好地掌握用户用电规律,辅助需求响应实施,还能显著降低算法的运行时间。

In view of the complex and changeable power consumption behavior of users under the background of power big data and the difficulty in analysis,a classification and analysis method of power consumption behavior of users based on undercomplete auto-encoder is proposed.Firstly,the data of intelligent electricity meters are encoded by an undercomplete auto-encoder to extract the features of the original data,and the back-propagation(BP)neural network is used to classify and analyze the user′s electricity consumption behavior.Then,the optimal coding ratio is selected,and the typical user electricity characteristics are taken as the input of the neural network to improve the classification accuracy.Finally,a simulation experiment is carried out on smart meters in Ireland data sets,compared with directly using the BP neural network analysis,the proposed method not only can improve the accuracy of detection,help electric power company to better grasp the power law of auxiliary demand response,but also can significantly reduce the running time of the algorithm.