Deep Learning-based Probabilistic Autoencoder for Residential Energy Disaggregation: An Adversarial Approach

Halil Cimen, Ying Wu, Yanpeng Wu, Yacine Terriche, Juan C. Vasquez, Josep M. Guerrero

Research output: Contribution to journalJournal articleResearchpeer-review

13 Citations (Scopus)


Energy disaggregation is the process of disaggregating a household's total energy consumption into its appliance-level components. One of the limitations of energy disaggregation is its generalization capacity, which can be defined as the ability of the model to analyze new households. In this article, a new energy disaggregation approach based on adversarial autoencoder (AAE) is proposed to create a generative model and enhance the generalization capacity. The proposed method has a probabilistic structure to handle uncertainties in the unseen data. By transforming the latent space from a deterministic structure to a Gaussian prior distribution, AAEs decoder transforms into a generative model. The proposed approach is validated through experimental tests using two different datasets. The experimental results exhibit a 55% MAE performance increase compared to deterministic models and 7% compared to probabilistic models. In addition, considering the predictions made when the appliances are on, the AAE improves the performance by 16% for UKDALE and 36% for REDD dataset compared to the state-of-art models. Moreover, the online analysis performance of AAE is examined in detail, and the disadvantages of instant predictions and the possible solutions are extensively discussed.

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Issue number12
Pages (from-to)8399-8408
Number of pages10
Publication statusPublished - Dec 2022

Bibliographical note

Publisher Copyright:


  • Adversarial autoencoder
  • Deep learning
  • Energy disaggregation
  • Generative adversarial networks
  • Nonintrusive load monitoring (NILM)
  • Online energy disaggregation
  • Probabilistic energy disaggregation
  • Residential energy disaggregation


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