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Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

Author

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  • Manuela Panoiu

    (Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 300006 Timișoara, Romania)

  • Caius Panoiu

    (Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 300006 Timișoara, Romania)

  • Petru Ivascanu

    (Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 300006 Timișoara, Romania)

Abstract

The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. A multi-step forecast was developed for the power factor in the power supply installation of a hot rolling mill, extending beyond the horizontal line. This was conducted using data obtained from the respective electrical supply system. The forecast was developed via hybrid RNN (recurrent neural networks) incorporating LSTM (long short-term memory) and GRU (gated recurrent unit) layers. This research utilized hybrid recurrent neural network designs with deep learning methods to build several power factor models. These layers have advantages for time series forecasting. After conducting time series forecasting, qualitative indicators of the prediction were identified, including the sMAPE (Symmetric Mean Absolute Percentage Error) and regression coefficient. In this paper, the authors examined the quality of applied models and forecasts utilizing these indicators, both in the short term and long term.

Suggested Citation

  • Manuela Panoiu & Caius Panoiu & Petru Ivascanu, 2024. "Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms," Mathematics, MDPI, vol. 12(6), pages 1-26, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:839-:d:1356114
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    References listed on IDEAS

    as
    1. Manuela Panoiu & Caius Panoiu & Sergiu Mezinescu & Gabriel Militaru & Ioan Baciu, 2023. "Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
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