Interpreting Atypical Conditions in Systems with Deep Conditional Autoencoders: The Case of Electrical Consumption

Antoine Marot*, Antoine Rosin, Laure Crochepierre, Benjamin Donnot, Pierre Pinson, Lydia Boudjeloud-Assala

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper, we propose a new method to iteratively and interactively characterize new feature conditions for signals of daily French electrical consumption from our historical database, relying on Conditional Variational Autoencoders. An autoencoder first learn a compressed similarity-based representation of the signals in a latent space, in which one can select and extract well-represented expert features. Then, we successfully condition the model over the set of extracted features, as opposed to simple target label previously, to learn conditionally independent new residual latent representations. Unknown, or previously unselected factors such as atypical conditions now appear well-represented to be detected and further interpreted by experts. By applying it, we recover the appropriate known expert features and eventually discover, through adapted representations, atypical known and unknown conditions such as holidays, fuzzy non working days and weather events, which were actually related to important events that influenced consumption.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
EditorsUlf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
PublisherSpringer
Publication date1 Jan 2020
Pages638-654
ISBN (Print)9783030461324
DOIs
Publication statusPublished - 1 Jan 2020
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Hubland Campus, University of Würzburg, Würzburg, Germany
Duration: 16 Sep 201920 Sep 2019

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
LocationHubland Campus, University of Würzburg
CountryGermany
CityWürzburg
Period16/09/201920/09/2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11908 LNAI
ISSN0302-9743

Keywords

  • Autoencoder
  • Interpretability
  • Representation

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