Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption

Tae Young Kim, Sung Bae Cho

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

1 Citation (Scopus)

Abstract

Recently, there have been many attempts to predict residential energy consumption using artificial neural networks. The optimization of these neural networks depends on the trial and error of the operator that lacks prior knowledge. They are also influenced by the initial values of the model based on the gradient algorithm and the size of the search space. In this paper, different kinds of hyperparameters are automatically determined by integrating particle swarm optimization (PSO) to CNN-LSTM network for forecasting energy consumption. Our findings reveal that the proposed optimization strategy can be used as a promising alternative prediction method for high prediction accuracy and better generalization capability. PSO achieves effective global exploration by eliminating crossover and mutation operations compared to genetic algorithms. To verify the usefulness of the proposed method, we use the household power consumption data in the UCI repository. The proposed PSO-based CNN-LSTM method explores the optimal prediction structure and achieves nearly perfect prediction performance for energy prediction. It also achieves the lowest mean square error (MSE) compared to conventional machine learning methods.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1510-1516
Number of pages7
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - 2019 Jun
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 2019 Jun 102019 Jun 13

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period19/6/1019/6/13

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All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Modelling and Simulation

Cite this

Kim, T. Y., & Cho, S. B. (2019). Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 1510-1516). [8789968] (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8789968