计算机科学
鉴定(生物学)
加权
人工智能
特征(语言学)
领域(数学)
模式识别(心理学)
特征提取
能量(信号处理)
工程类
数学
生物
语言学
哲学
植物
医学
统计
纯数学
放射科
作者
Haitao Wang,Peng Wang,Liang Shu
标识
DOI:10.1088/1361-6501/ad3978
摘要
Abstract Non-intrusive load monitoring is a significant advancement in energy conservation and smart electricity usage as it enables the identification of load status and equipment type without the need for extensive sensing instruments. As one of the important steps of non-intrusive load monitoring, non-intrusive load identification plays a crucial role in the correct identification of electrical appliances. In this paper, a novel approach is presented to amalgamate multiple appliance features into a new identification feature, which is then used for appliance recognition through an improved lightweight deep learning model. This addresses the issue of low accuracy when using a single feature for appliance identification. The core concept involves initially capturing the current data of an appliance during steady state operation and fusing the image features of the appliance encoded by Gramian summation angular field, Gramian difference angular field, and Markov transition field into new recognition features for each appliance using average weighting. Subsequently, a shufflenetv2 lightweight deep learning model based on the squeeze -and-excitation module is used to mine the constructed load feature information for the load classification task. The method is then experimentally validated on the Self-test Dataset, PLAID, and WHITED dataset, resulting in recognition accuracies of 100%, 98.214%, and 99.745%, respectively. These results demonstrate the effectiveness of the proposed method in significantly improving recognition performance compared to the original approach.
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