Non-Intrusive Adaptive Load Identification Based on Siamese Network

Miao Yu,Donglian Qi,Lingxia Lu,Zhejing Bao,Bingnan Wang

doi:10.1109/access.2022.3145982

Abstract

The traditional non-intrusive load monitoring (NILM) algorithms are mostly based on classification models, which have several deficiencies. Firstly, a large amount of labeled data is required to train the classification model. Secondly, these algorithms cannot identify unknown devices that frequently encountered in practical application. Finally, these models have poor performance in versatility, which means they only adapt to the trained data. These shortcomings greatly influence the practicality of these NILM algorithms. To tackle these problems, this paper has proposed a non-intrusive adaptive load identification model based on the Siamese network, which uses both the V-I trajectory and active power as the load signatures. The Siamese network is utilized to calculate the similarity of the V-I trajectory, and the load identification is realized by matching the signature with the feature library. Through adding new features to the feature library dynamically, the identification of unknown load can be realized. In addition, the Siamese network is a typical network for few-shot learning, thus the proposed model can be trained with a small number of samples to achieve ideal recognition effect. At last, the validity and versatility of the model are verified in PLAID dataset and COOLL dataset.

Highlights

Electricity, as the main secondary energy source, is the major way of energy consumption and closely related to our lives
In addition to the F1 score, in order to explore the relationship between the number of codes and the accuracy of each electrical appliance, if an electrical appliance has m states, ideally, when the number of allowable numbers cnt=m, the accuracy reaches 100%, that is, acc(m)=100%
This paper has proposed a non-intrusive adaptive load identification algorithm based on Siamese network, and verified its performance through PLAID and COOLL datasets

Summary

INTRODUCTION

Electricity, as the main secondary energy source, is the major way of energy consumption and closely related to our lives. In this paper, we have proposed a model based on the Siamese network, which is a kind of few-shot learning. [15] proposed a two-stream convolutional neural network based on current time-frequency feature fusion for nonintrusive load identification. B. Few-Shot Learning (FSL) some of the above algorithms can achieve ideal results in precision, these methods require a large amount of label data to train the model, and it is usually difficult to obtain sufficient training data in practice. In the field of image processing, humans can extract key information from a small number of samples, while traditional neural networks require a large amount of training data to learn feature representation methods.

SIAMESE NETWORK

EXPERIMENTAL EVALUATION

PERFORMANCE MATRICS

Findings

CONCLUSION