.png){kind=icon}
返回
Data-Driven Strategy for Appliance Identification Using Phase-Space Reconstruction
.png){kind=icon}
PRE
AI
DOI：10.1109/TSG.2023.3300584.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The present work proposes to utilize a data-driven technique, namely, phase space reconstruction (PSR), followed by an exploration of multiple delays, which, in turn, enables the generation of multiple distinct image sets for each of the load signatures without involving data-normalization. Considering the multiple generated images, individual convolutional neural networks (CNNs) for each of the images are considered, resulting in variegated predictions which are effectively combined within a majority voting framework in order to limit the erroneous predictions of the individual classifiers. The proposed strategy leads to significant improvement in classification accuracy compared to the relevant existing literature.
Keyword:
Appliance identification
load signature
phase space reconstruction
deep learning
majority voting
non-intrusive load monitoring
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
分享收藏A residual convolutional neural network with multi-block for appliance recognition in non-intrusive load identification.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏分享收藏Temporal and Spectral Feature Learning With Two-Stream Convolutional Neural Networks for Appliance Recognition in NILM在NILM中使用两流卷积神经网络进行时间和光谱特征学习以识别电器
PREAI
分享收藏Topological Data Analysis Approach to Extract the Persistent Homology Features of Ballistocardiogram Signal in Unobstructive Atrial Fibrillation DetectionIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
没有更多内容
没有更多内容