Anomaly detection based on joint spatio-temporal learning for building electricity consumption-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Anomaly detection based on joint spatio-temporal learning for building electricity consumption

Published:2023-03 Issue: Volume:334 Page:120635
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Kong Jun,Jiang Wen,Tian Qing,Jiang Min,Liu Tianshan

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Higher Education Discipline Innovation Project

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference37 articles.

1. Heating and cooling energy trends and drivers in buildings;Ürge Vorsatz;Renew Sustain Energy Rev,2015

2. Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data;Fan;Appl Energy,2018

3. A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data;Xu;Energy Build,2020

4. Fraud detection in electric power distribution: An approach that maximizes the economic return;Massaferro;IEEE Trans Power Syst,2020

5. Solutions for detection of non-technical losses in the electricity grid: A review;Viegas;Renew Sustain Energy Rev,2017

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization;Energy;2024-10

2. Enhancing Anomaly Detection in Critical Systems Using Household Appliance Power Consumption Data;IEEE Sensors Journal;2024-09-01

3. Two-stage generalizable approach for electricity theft detection in new regions;Applied Energy;2024-07

4. A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm;Smart Grids and Sustainable Energy;2024-06-27

5. A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework;Frontiers in Energy Research;2024-04-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3