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

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    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 2019 - Hubland Campus, University of Würzburg, Würzburg, Germany
    Duration: 16 Sept 201920 Sept 2019
    https://ecmlpkdd2019.org/

    Conference

    ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2019
    LocationHubland Campus, University of Würzburg
    Country/TerritoryGermany
    CityWürzburg
    Period16/09/201920/09/2019
    Internet address
    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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