Environmental Impacts on Energy Utilization of Smart Grid Consumers

Khan, Muhammad Yasir Ali; Saeed, Laraib; Zeb, Babar; Ahmed, Naseer; Azhar, Muhammad; Rustam, Muhammad Asfand Yar; Basit, Abdul; Soomro, Jahangeer Badar; Khan, Safi Ullah

doi:10.1109/icomet.2019.8673441

Cited by 1 publication

(1 citation statement)

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“…Similarly to [45], there is a significant impact of temperature variability on electricity consumption despite different locations. This article uses similar analysis methods, but they were used for different types of objects.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Energy Consumption in Railway Signal Boxes

Kampik,

Bodzek,

Piaskowy

et al. 2023

Energies

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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.

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Section: Discussionmentioning

confidence: 99%