Non-intrusive load monitoring is an important research direction in the field of intelligent power utilization

and the load identification is the core of non-intrusive load monitoring. The load feature library is built based on the load amplitudes of the odd harmonic current. An AdaBoost sample screening algorithm is designed to simplify the load feature library through analyzing the distribution of load samples in the feature space. By using the simplicity of k-nearest neighbor(k-NN)classification algorithm and the nonlinear classification ability of the kernel Fisher discriminant algorithm

a method combined k-NN with kernel Fisher discriminant is proposed for load identification by controlling the risk of misclassification. The identification accuracy and computational complexity are considered in the method aforementioned to improve the identification ability of loads with similar characteristics and the speed of identification. The measured data shows that the proposed method can realize the resident load identification quickly and accurately.