An Analysis of Energy Consumption in Railway Signal Boxes

Marian Kampik (), Krzysztof Bodzek, Anna Piaskowy, Adam Pilśniak and Marcin Fice ()
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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

Energies, 2023, vol. 16, issue 24, 1-21

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.

Keywords: railway signal box; correlation analysis; energy consumption profiles; limitation of probabilistic method; data normalization; Pearson’s correlation coefficient; Spearman’s correlation coefficient; scatter plot; distance covariance test (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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