A deep learning approach for anomaly detection and prediction in power consumption data-Reference-Cited by-同舟云学术

A deep learning approach for anomaly detection and prediction in power consumption data

Published:2020-08-07 Issue:8 Volume:13 Page:1633-1651
ISSN:1570-646X
Container-title:Energy Efficiency
language:en
Short-container-title:Energy Efficiency

Author:

Chahla C.^ORCID,Snoussi H.,Merghem L.,Esseghir M.

Funder

Conseil Régional Champagne Ardenne

Publisher

Springer Science and Business Media LLC

Subject

General Energy

Link

https://link.springer.com/content/pdf/10.1007/s12053-020-09884-2.pdf

Reference17 articles.

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3. Abraham, B., & Box, G.E.P. (1979). Bayesian analysis of some outlier problems in time series. Biometrika, 66(2), 229–236. 10.1093/biomet/66.2.229.

4. Abraham, B., & Chuang, A. (1989). Outlier detection and time series modeling. Technometrics, 31(2), 241–248. https://doi.org/10.1080/00401706.1989.10488517. https://amstat.tandfonline.com/doi/abs/10.1080/00401706.1989.10488517.

5. Antmann, P. (2009). Reducing technical and non-technical losses in the power sector. World Bank. http://documents.worldbank.org/curated/en/829751468326689826.

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