Electric energy consumption prediction by deep learning with state explainable autoencoder

Jin Young Kim, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


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.

Original languageEnglish
Article number739
Issue number4
Publication statusPublished - 2019 Feb 22

Bibliographical note

Funding Information:
Acknowledgments: This research was supported by Korea Electric Power Corporation (Grant number: R18XA05).

Publisher Copyright:
© 2019 by the authors.

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Energy (miscellaneous)
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment


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