基于seq2seq和SVM双层融合的非侵入式用户异常行为检测

NON-INTRUSIVE ABNORMAL USER BEHAVIOR DETECTION BASED ON DOUBLE-LAYER FUSION OF SEQ2SEQ AND SVM

以非侵入式负荷分解为基础,对用户异常用电行为进行研究。采用Kmeans聚类算法提取负荷状态特征;采用深度学习算法中的序列到序列翻译(sequence to sequence, seq2seq)模型,将电力用户用电总数据分解成单个电器的功耗数据;结合SVM算法对分解后多种家用电器用电数据进行异常检测。在UKDALE数据集实验结果表明,该模型不仅能提高分解准确度、降低分解误差,而且多个电器数据结合分析实现了用户异常行为检测。

The paper studies the abnormal power consumption behavior of customers on the basis of non-intrusive load disaggregation.K-means clustering algorithm was used to extract the state features of the loads,and the sequence-to-sequence model of deep learning algorithm was used to disaggregate the total power consumption data of power users into power consumption data of a single equipment.Combining with SVM algorithm,the abnormal data of many kinds of household electrical appliances were analyzed.Experiments on UKDALE dataset show that the proposed model can not only improve the disaggregation accuracy and reduce the disaggregation error,but also can monitor the abnormal behavior of users.