A deep learning approach for anomaly detection and prediction in power consumption data
Crossref DOI link: https://doi.org/10.1007/s12053-020-09884-2
Published Online: 2020-08-07
Published Print: 2020-12
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Chahla, C. http://orcid.org/0000-0002-1610-8564
Snoussi, H.
Merghem, L.
Esseghir, M.
Funding for this research was provided by:
Conseil RĂ©gional Champagne Ardenne
Text and Data Mining valid from 2020-08-07
Version of Record valid from 2020-08-07
Article History
Received: 14 March 2019
Accepted: 13 July 2020
First Online: 7 August 2020