一种融合时间特征的非侵入式负荷辨识决策方法

针对家庭负荷用电场景中负荷类别的不确定性，以及非侵入式负荷监测设备数据库中负荷特征库的不完备等极易导致负荷辨识准确率下降的问题，文中在利用电气特征的基础上，提出了一种融合负荷运行时长、运行时段、工作周期及假期特性等时间特征的非侵入式负荷辨识决策方法。在该方法中，通过分段归一化的Mean-shift聚类方法对检测得到的负荷事件特征进行聚类统计，获取潜在的负荷类别数；对用电设备负荷事件的时间特性进行统计，同时计算负荷功率特征度量负荷事件所产生的概率，并采用贝叶斯方法对负荷进行决策辨识。采用AMPds公共数据集进行实际测试，实验结果表明该方法对该场景具有较好的辨识效果。

Non-intrusive load identification decision method based on time signatures

Considering the problems of the uncertainty of the load type in household scenario and the incompleteness of the load signature database in the non-intrusive load database, which can easily lead to the decrease of the accuracy in load identification, this paper proposes a load identification method to cope with these problems. On the base of electrical signatures, this method also use time signature which includes the characteristics of the length of operation time, load operation time, working period and vacation. In this method, firstly, we use the piecewise- normalization mean-shift clustering method to cluster the detected load event features and obtain the number of potential load types. Then we count the time signature and power signature of load events to get their probability. And the Bayesian method is used to identify the load by decision-making. Finally, this paper uses the AMPds public data set to do the actual test, the experimental results show that this method has the good identification effect to this scene.