浙江电力2024，Vol.43Issue(6)：88-100,13.DOI:10.19585/j.zjdl.202406010
基于数据扩充与无阈值递归图的非侵入式负荷识别方法
A non-intrusive load identification method based on data augmentation and threshold-free recurrence plot
摘要
Abstract
Non-intrusive load monitoring(NILM)not only makes the flow of electric energy transparent but also sim-plifies the installation process of smart meters,effectively reducing the cost of load monitoring.To enhance the accu-racy of load recognition in NILM,a method for load recognition based on data augmentation and threshold-free re-currence plot(RP)is proposed.a denoising diffusion probability model(DDPM)is utilized to augment the load data of small samples to enhance the robustness of the load recognition method.Furthermore,a threshold-free RP,achieved by removing the Heaviside function of the recurrence graph,efficiently represents load characteristics.This is combined with a Transformer deep learning network to construct a load recognition framework.The proposed method is applied to three real-world datasets,and experimental results demonstrate its effectiveness in improving load recognition accuracy and enhancing classification performance.关键词
非侵入式负荷监测/数据扩充/负荷识别/深度学习/递归图Key words
NILM/data augmentation/load identification/deep learning/RP引用本文复制引用
邢海青,郭瑞峰,杨浙川,熊小雨,施永涛..基于数据扩充与无阈值递归图的非侵入式负荷识别方法[J].浙江电力,2024,43(6):88-100,13.基金项目
浙江省重点研发计划(2021C01144) （2021C01144）
浙江大有集团有限公司科技项目(2021-DY16) （2021-DY16）
