Abstract: 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.

Key words: non-intrusive load monitoring, load identification, kNN algorithm, cosine similarity