Analysis of household power consumption data for social safety net services

被引：0

作者：

Kim, Gyubaek ^{[1
,2
]}

Park, Sanghyun ^{[2
]}

机构：

[1] SK Telecom, AI Robot Dev Team, 65 Eulji Ro Jung Gu, Seoul 04539, South Korea

[2] Yonsei Univ, Dept Comp Sci, 50 Yonsei Ro Seodaemun Gu, Seoul 03722, South Korea

来源：

ENERGY REPORTS | 2023年 / 9卷

关键词：

Lonely death; Activity of daily living; Activity perception; Anomaly detection; Appliance activation profile; Power disaggregation;

D O I：

10.1016/j.egyr.2023.04.259

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Much interest in lonely death and activity of daily living (ADL) monitoring services through analysis from household power consumption data is increasing. For this, anomaly detection and power disaggregation are needed, respectively. However, the existing technologies suffer from inaccuracy problem, so they are not widely used. In this study, activity perception-based anomaly detection and appliance activation profile-based disaggregation methods are newly presented to improve accuracy. According to the experimental results, the proposed methods showed better performance than the existing methods. (c) 2023 The Author(s). Published by Elsevier Ltd.

引用

页码：18 / 24

页数：7

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