Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention

João Pereira, Margarida Silveira

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

29 Citations (Scopus)

Abstract

In the age of big data, time series are being generated in massive amounts. In the energy field, smart grids are enabling a unprecedented data acquisition with the integration of sensors and smart devices. In the context of renewable energies, there has been an increasing interest in solar photovoltaic energy generation. These installations are often integrated with smart sensors that measure the energy production. Such amount of data collected makes the quest for developing smart monitoring systems that can detect anomalous behaviour in these systems, trigger alerts and enable maintenance operations. In this paper, we propose a generic, unsupervised and scalable framework for anomaly detection in time series data, based on a variational recurrent autoencoder. Furthermore, we introduce attention in the model, by means of a variational self-attention mechanism (VSAM), to improve the performance of the encoding-decoding process. Afterwards, we perform anomaly detection based on the probabilistic reconstruction scores provided by our model. Our results on solar energy generation time series show the ability of the proposed approach to detect anomalous behaviour in time series data, while providing structured and expressive representations. Since it does not need labels to be trained, our methodology enables new applications for anomaly detection in energy time series data and beyond.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
EditorsM. Arif Wani, Moamar Sayed-Mouchaweh, Edwin Lughofer, Joao Gama, Mehmed Kantardzic
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1275-1282
Number of pages8
ISBN (Electronic)978-1-5386-6805-4
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
Duration: 17 Dec 201820 Dec 2018

Conference

Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
CountryUnited States
CityOrlando
Period17/12/1820/12/18

Keywords

  • Anomaly Detection
  • Attention
  • Solar Photovoltaic Energy
  • Variational Recurrent Autoencoder

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