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An Analysis of Energy Consumption in Railway Signal Boxes

Author

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  • Marian Kampik

    (Department of Measurement Science, Electronics and Control, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Krzysztof Bodzek

    (Department of Power Electronics, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Anna Piaskowy

    (Department of Measurement Science, Electronics and Control, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Adam Pilśniak

    (Department of Measurement Science, Electronics and Control, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Marcin Fice

    (Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson’s method, Spearman’s method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson’s and Spearman’s coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson’s and Spearman’s coefficients for all analysed objects range from approximately 0.7 to over 0.9.

Suggested Citation

  • Marian Kampik & Krzysztof Bodzek & Anna Piaskowy & Adam Pilśniak & Marcin Fice, 2023. "An Analysis of Energy Consumption in Railway Signal Boxes," Energies, MDPI, vol. 16(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7985-:d:1297017
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    References listed on IDEAS

    as
    1. Ningxin Zhang & Yu Zhang & Hanli Chen, 2023. "Spatial Correlation Network Structure of Carbon Emission Efficiency of Railway Transportation in China and Its Influencing Factors," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    2. Trinks, Arjan & Mulder, Machiel & Scholtens, Bert, 2020. "An Efficiency Perspective on Carbon Emissions and Financial Performance," Ecological Economics, Elsevier, vol. 175(C).
    3. Kyoungho Ahn & Ahmed Aredah & Hesham A. Rakha & Tongchuan Wei & H. Christopher Frey, 2023. "Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications," Energies, MDPI, vol. 16(8), pages 1-15, April.
    4. Kui Yang & Bofu Wang & Xiang Qiu & Jiahua Li & Yuze Wang & Yulu Liu, 2022. "Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit," Energies, MDPI, vol. 15(12), pages 1-24, June.
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