An Analysis of Energy Consumption in Railway Signal Boxes
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 mo...
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MDPI AG
2023-12-01
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Series: | Energies |
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Online Access: | https://www.mdpi.com/1996-1073/16/24/7985 |
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author | Marian Kampik Krzysztof Bodzek Anna Piaskowy Adam Pilśniak Marcin Fice |
author_facet | Marian Kampik Krzysztof Bodzek Anna Piaskowy Adam Pilśniak Marcin Fice |
author_sort | Marian Kampik |
collection | DOAJ |
description | 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. |
first_indexed | 2024-03-08T20:49:16Z |
format | Article |
id | doaj.art-e8c28da1016345d68631c1e945fc66ce |
institution | Directory Open Access Journal |
issn | 1996-1073 |
language | English |
last_indexed | 2024-03-08T20:49:16Z |
publishDate | 2023-12-01 |
publisher | MDPI AG |
record_format | Article |
series | Energies |
spelling | doaj.art-e8c28da1016345d68631c1e945fc66ce2023-12-22T14:05:43ZengMDPI AGEnergies1996-10732023-12-011624798510.3390/en16247985An Analysis of Energy Consumption in Railway Signal BoxesMarian Kampik0Krzysztof Bodzek1Anna Piaskowy2Adam Pilśniak3Marcin Fice4Department of Measurement Science, Electronics and Control, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Power Electronics, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Measurement Science, Electronics and Control, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Measurement Science, Electronics and Control, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Electrical Engineering and Computer Science, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, PolandThis 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.https://www.mdpi.com/1996-1073/16/24/7985railway signal boxcorrelation analysisenergy consumption profileslimitation of probabilistic methoddata normalizationPearson’s correlation coefficient |
spellingShingle | Marian Kampik Krzysztof Bodzek Anna Piaskowy Adam Pilśniak Marcin Fice An Analysis of Energy Consumption in Railway Signal Boxes Energies railway signal box correlation analysis energy consumption profiles limitation of probabilistic method data normalization Pearson’s correlation coefficient |
title | An Analysis of Energy Consumption in Railway Signal Boxes |
title_full | An Analysis of Energy Consumption in Railway Signal Boxes |
title_fullStr | An Analysis of Energy Consumption in Railway Signal Boxes |
title_full_unstemmed | An Analysis of Energy Consumption in Railway Signal Boxes |
title_short | An Analysis of Energy Consumption in Railway Signal Boxes |
title_sort | analysis of energy consumption in railway signal boxes |
topic | railway signal box correlation analysis energy consumption profiles limitation of probabilistic method data normalization Pearson’s correlation coefficient |
url | https://www.mdpi.com/1996-1073/16/24/7985 |
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