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Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting

Author

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  • Pavel V. Matrenin

    (Industrial Power Supply Systems Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Vadim Z. Manusov

    (Industrial Power Supply Systems Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Alexandra I. Khalyasmaa

    (Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
    Power Plants Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Dmitry V. Antonenkov

    (Industrial Power Supply Systems Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Stanislav A. Eroshenko

    (Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
    Power Plants Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Denis N. Butusov

    (Youth Research Institute, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia)

Abstract

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset.

Suggested Citation

  • Pavel V. Matrenin & Vadim Z. Manusov & Alexandra I. Khalyasmaa & Dmitry V. Antonenkov & Stanislav A. Eroshenko & Denis N. Butusov, 2020. "Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting," Mathematics, MDPI, vol. 8(12), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2169-:d:457151
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    References listed on IDEAS

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