一个具有三点标记和非因果卷积技术的高性价比NILM解决方案

IF 2.7 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2025-09-24 DOI:10.1049/stg2.70036
Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo
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引用次数: 0

摘要

尽管深度学习在非侵入式负载监测(NILM)领域越来越有前途,但数据记录和标记的高成本对监督模型的训练构成了重大挑战。为了解决这个问题,提出了一种具有成本效益的序列到点NILM解决方案,将三点标记与非因果卷积技术相结合。该方法引入了用于获取NILM三点数据的半自动标记框架,为大规模应用提供了低成本的数据收集和标记解决方案。然后,提出了一种结合坐标损失和置信度损失的损失函数,解决了NILM中序列到点场景下的位置错位和负样本混淆问题。此外,设计了一种基于多尺度非因果时间卷积技术的先进神经网络架构,以捕获不同设备的独特特征和运行模式。在UK-DALE数据集上的实验结果表明,在序列到点的情况下,所提出的混合损失函数比普通的平均绝对误差(MAE)具有优势,并且新网络在所有设备上都表现出色,证明了它在实际NILM应用中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Cost-Effective NILM Solution With Three-Point Labelling and Non-Causal Convolution Technique

A Cost-Effective NILM Solution With Three-Point Labelling and Non-Causal Convolution Technique

Although deep learning is increasingly promising in the field of Non-Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost-effective sequence-to-points NILM solution is proposed, integrating three-point labelling with non-causal convolution techniques. The approach introduces a semi-automatic labelling framework for obtaining NILM three-point data, which provides a low-cost data collection and labelling solution for large-scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence-to-points scenario in NILM. Furthermore, an advanced neural network architecture based on multi-scale non-causal temporal convolution techniques is designed to capture unique features and operational modes of different appliances. Experimental results on the UK-DALE dataset show that the proposed mixed loss function has an advantage over plain Mean Absolute Error (MAE) on the sequence-to-points occasion, and the novel network outperforms on all of the appliances, demonstrating its potential for practical NILM applications.

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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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