基于改进kNN算法与暂稳态特征的非侵入式负荷监测方法

非侵入式负荷监测（Non-intrusive Load Monitoring, NILM）通过分析电力总回路的电气信息得到回路中各电器的运行数据，为用户的节能优化和电网的优化调度提供依据。现有NILM方法主要将研究重点放在提高负荷识别准确度上，模型复杂度高，难以在嵌入式设备上应用。针对上述问题，提出一种基于改进kNN算法与暂稳态特征的NILM方法。首先选择无需训练的kNN算法作为负荷识别模型，采用距离权重统计方法对kNN算法进行改进，并增加余弦相似度判断机制检验kNN算法负荷识别结果准确性；然后选择暂态特征和稳态特征作为负荷特征以提高负荷特征辨识度；最后利用实验采集数据进行验证，上述NILM方法具有良好的性能。

A Non-intrusive Load Monitoring Method Based on Improved kNN Algorithmand Transient Steady State Features

Non-intrusive load monitoring (NILM) can obtain the operation data of the electrical appliance in the circuit by analyzing the record from a single energy meter, which can serve as an important tool for energy saving planning and optimal dispatching for power grid. The existing NILM methods mainly focus on improving the accuracy of load identification, the model complexity is too high to be applied on embedded devices. A NILM method based on improved kNN algorithm and transient steady state feature is proposed to solve the above problems. Firstly, the kNN algorithm is selected as the load identification model because it does not require training, the kNN algorithm is improved by statistical method of distance weight, and the cosine similarity judgment mechanism is added to verify the accuracy of the kNN load identification results. Secondly, the transient and steady state features are selected as load characteristics to improve the identification of load features. Finally, experimental data are used to verify that the above NILM method has superior performance.