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Interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) (SOCO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

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.

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Acknowledgement

This research was supported by Korea Electric Power Corporation (Grant number: R18XA05). J. Y. Kim has been supported by NRF (National Research Foundation of Korea) grant funded by the Korean government (NRF-2019-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program).

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Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Seoul, South Korea

    Jin-Young Kim & Sung-Bae Cho

Authors
  1. Jin-Young Kim
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  2. Sung-Bae Cho
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Corresponding author

Correspondence to Sung-Bae Cho .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Kim, JY., Cho, SB. (2021). Interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_13

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