Abstract
<!--StartFragment --> 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(x<sub>t</sub>|Y<sup>t</sup>). 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.
Highlights
Non-intrusive load monitoring (NILM) was proposed by Hart in the 1980s [1]
An elegant solution to the problem of optimal sampling from the posterior has been given by Pitt and Shephard [6] under the name‘auxiliary particle filter’
We found that the efficiency of particle filter is mainly determined by the number of particles used.With the increase of the number of particles, efficiency is higher.But in this paper,the particle filter algorithm is not improved,which is the need to improve this article
Summary
Which decomposes the total load information into electrical equipment information, and obtains the energy consumption situation and the electricity consumption law of the users These electricity information has a high application value. Many new methods of nonlinear filtering have been proposed in signal transmission and compression, financial data analysis, image processing, load monitoring and decomposition. All of these algorithms are based on the sequential importance sampling (SIS) filter of Bayesian sampling estimation. When the number of samples is very large, the probability estimation will be equal to the posterior probability density This method is an unsupervised classification method which is suitable for nonlinear and non Gauss interference problems
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