Interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption

Jin Young Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As energy demand continues to increase, smart grid systems that perform efficient energy management become increasingly important due to environmental and cost reasons. It requires faster prediction of electric energy consumption and valid explanation of the predicted results. Recently, several demand predictors based on deep learning that can deal with complex features of data are actively investigated, but most of them suffer from lack of explanation due to the black-box characteristics. In this paper, we propose a hybrid autoencoder-based deep learning model that predicts power demand in minutes and also provides the explanation for the predicted results. It consists of an information projector that uses auxiliary information to extract features for the current situation and a model that predicts future power demand. This model exploits the latent space composed of the two different modalities to account for the prediction. Experiments with household electric power demand data collected over five years show that the proposed model is the best with a mean squared error of 0.3764. In addition, by analyzing the latent variables extracted by the information projector, the correlation with various conditions including the power demand is confirmed to provide the reason of the coming power demand predicted.

Original languageEnglish
Title of host publication15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020
EditorsÁlvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado
PublisherSpringer Science and Business Media Deutschland GmbH
Pages133-143
Number of pages11
ISBN (Print)9783030578015
DOIs
Publication statusPublished - 2021
Event15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 - Burgos, Spain
Duration: 2020 Sep 162020 Sep 18

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1268 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020
CountrySpain
CityBurgos
Period20/9/1620/9/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Kim, J. Y., & Cho, S. B. (2021). Interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption. In Á. Herrero, C. Cambra, D. Urda, J. Sedano, H. Quintián, & E. Corchado (Eds.), 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 (pp. 133-143). (Advances in Intelligent Systems and Computing; Vol. 1268 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57802-2_13