Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption-Reference-Cited by-同舟云学术

Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption

Published:2022-12-18 Issue: Volume: Page:
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Container-title:2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
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Author:

Copiaco Abigail¹,Himeur Yassine¹,Amira Abbes²,Mansoor Wathiq¹,Fadli Fodil³,Atalla Shadi¹

Affiliation:

1. College of Engineering and IT, University of Dubai,Dubai,UAE

2. University of Sharjah,Department of Computer Science,UAE

3. Qatar University,Department of Architecture & Urban Planning,Doha,Qatar,2713

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10089231/10089220/10089265.pdf?arnumber=10089265

Reference38 articles.

1. Detection of appliance-level abnormal energy consumption in buildings using au-toencoders and micro-moments;himeur;Proceedings of the 5th International Conference on Big Data and Internet of Things,2022

2. Individual household electric power consumption dataset;bache;Irvine CA University of California School of Information and Computer Science,2013

3. A Two-Stage Energy Anomaly Detection for Edge-based Building Internet of Things (BIoT) Applications

4. LocED

5. An Outlier Detection Algorithm for Electric Power Data Based on DBSCAN and LOF

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