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

    35 Citations (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
    Country/TerritoryNew Zealand
    CityWellington
    Period19/6/1019/6/13

    All Science Journal Classification (ASJC) codes

    • Computational Mathematics
    • Modelling and Simulation

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