【摘要】 针对家用负荷提出了一种使用智能电表进行数据采集的非侵入式负荷在线识别方法。该方法使用智能电表计算出负荷的差量特征向量预先建立特征库，训练以决策树作为弱分类器的AdaBoost分类器模型，利用负荷投切时电表的告警信息中包含的特征向量进行分类以实现负荷在线识别，实时性好且提高了单一决策树模型的识别效果。实验结果证明了该方法的可行性，实现了负荷使用信息的获取，具有较好的实际应用价值。更多还原

【Abstract】 Aiming at household load, a non-intrusive online load identification method using smart meters for data collection is proposed. This method uses the smart meter to calculate the difference feature vector of the load to build a feature library in advance, trains the AdaBoost classifier model which takes the decision tree as the weak classifier, and uses the feature vector contained in the alarm information of the smart meter when the load is switched to classify the load, and to achieve load online recognition. This method has good real-time performance and improves the recognition effect of a single decision tree model. The experimental results show that the proposed method is feasible, and realizes the acquisition of load usage information, has good practical application value.更多还原