.png){kind=icon}
返回
Feature Extraction for Load Identification Using Long-Term Operating Waveforms
.png){kind=icon}
OA
AI
DOI：10.1109/TSG.2014.2373314.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a novel finite-state-machine (FSM) representation of long-term load operating waveforms for feature extraction and load identification. An operating waveform is first converted into a quantized sequence of states. Each state is assigned with 2-D numerical values: root mean square (RMS) current values and staying time values. A set of elemental states and events are defined to reduce the number of states and extract numerical features to represent electric loads for classification and identification. Three major categories of repeating patterns in waveforms that correspond to repeating operating actions are summarized and identification methods are proposed for each such category. Test results using a large dataset of real-world waveforms show that the different appliances have distinct ranges of features extracted from the proposed FSM representation, and thus can be identified with high accuracy.
Keyword:
Direct load control
energy management
feature extraction
load identification
mode extraction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
.png){kind=icon}
Dynamic preload control of traveling wave rotary ultrasonic motors for energy efficient operation
PREAI
分享收藏分享收藏Front-End Electronic Circuit Topology Analysis for Model-Driven Classification and Monitoring of Appliance Loads in Smart Buildings前端电子电路拓扑分析，用于模型驱动的分类和监视智能建筑中的电器负载
PREAI
分享收藏
查看更多内容