Abstract:Non-intrusive load monitoring (NILM) is one of the important development directions of power grid construction in China in the future. In order to overcome the problems of large amount of calculation data and low identification accuracy of traditional NILM methods, a non-intrusive load monitoring method based on CFSFDP (clustering by fast search and find of density peaks) graph Laplace algorithm was proposed in this paper. Firstly, the power threshold vector and the prior graph structure were constructed using the active power data adopting the CFSFDP algorithm. Then, the graph Laplacian quadratic optimal function was constructed by combining the total power signal and graph signal smoothness, and the optimal solution was obtained iteratively by Tikhonov regularization method, so as to realize the reconstruction of graph signal of appliance. Finally, the graph signals were converted into power signals according to the power threshold vector, which enabled non-intrusive load monitoring. The following results were obtained from the simulation analysis of two days of measured electricity consumption data of a real household，including the load monitoring results within two days and the impact of sampling frequency on the algorithm performance. 1) The proposed method can identify all the equipments running within the first day, and the calculated proportion of electricity consumed by each electric equipment is close to the actual situation. 2) The load identification accuracy of the proposed method for the next day is 90.1%, which is superior to four comparison methods. The decomposition accuracy of a single appliance is more than 91%, and the vast majority of devices have lower power consumption error than the comparison methods. 3) When the data sampling interval is increased to 2 min, although the precision, identification accuracy and single appliance decomposition accuracy of the proposed method are all reduced, its calculation results gains superior performance and time complexity than the comparison methods. The simulation results verify the effectiveness of the proposed non-intrusive load monitoring method and its superiority for solving practical low-frequency NILM problems.