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Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

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  • Jin-Young Kim

    (Department of Computer Science, Yonsei University, Seoul 03722, Korea)

  • Sung-Bae Cho

    (Department of Computer Science, Yonsei University, Seoul 03722, Korea)

Abstract

As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 min with 60-min demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

Suggested Citation

  • Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:739-:d:208440
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    References listed on IDEAS

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    10. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
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    14. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    15. Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
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