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Research on Household Appliances Recognition Method Based on Data Screening of Deep Learning

机译：基于数据筛选深度学习的家用电器识别方法研究

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摘要

The Non-intrusive household load indentification can realize a series of power quality analysis such as power management, energy monitoring. It has the advantages of low cost, easy implementation. Aiming at the problem that a large number of V-I trajectory sample data is unavailable due to noise interference of stable operation data of household appliances of the actual measurement, a V-I map sample data set screening algorithm is proposed, which screen the two-dimensional V-I feature map data sets of characterizing household appliances, improves the deep learning network, and achieves better recognition effect of household appliances by using transfer learning. Experiments show that this method can effectively improve the accuracy of load identification algorithm, and has more advantages than traditional methods.
机译：非侵入式家用负载缩进可以实现一系列电力质量分析，如电源管理，能量监测。它具有低成本，实施方便的优点。针对大量VI轨迹样本数据由于实际测量的家用电器稳定运行数据的噪声干扰而无法使用的问题，提出了VI地图样本数据集筛选算法，该屏幕是二维VI功能地图数据集特征家用电器，改善了深度学习网络，通过转移学习实现了家用电器的更好识别效果。实验表明，该方法可以有效提高负载识别算法的准确性，并且具有比传统方法更具优势。

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