Load Monitoring Based on the Auxiliary Particle Filter Algorithm

This paper introduces family load monitoring based on auxiliary particle filter algorithm. It mainly uses a set of random samples with relevant weights to estimate the posterior probability density p(xt|Yt). First of all, the model of household electrical appliances is established in this paper, then using the particle filter algorithm to estimate the state. It mainly consists of two parts: including Bayesian estimation and auxiliary particle filter-based load monitoring. Finally, the data collected by the sensor is simulated on the MATLAB platform, and the simulation results are obtained by using the evolutionary auxiliary particle filter algorithm.