Abstract
To achieve high-precision load recognition for residential electricity consumption and to solve the transmission problem of collecting large amounts of current data from household appliances by smart meters, a data transmission method based on compressed sensing is proposed to reduce data transmission costs. Firstly, a dictionary learning algorithm is used to sparsely represent the current data of household appliances, and the over-complete dictionary matrix is used as the sparse matrix; then a random Gaussian matrix is used as the measurement matrix for data transmission, and finally, regular orthogonal matching pursuit algorithm is used to complete the data reconstruction. The experimental results show that the reconstruction effect of this method is better than the traditional reconstruction method under the same compression ratio.
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