An Analysis of Energy Consumption in Railway Signal Boxes

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.

Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism

“…The allocation and planning of urban basic charging service facilities present a comprehensive challenge, involving multi-source data and complex scenarios. With the growing adoption of deep learning (DL) methods, research findings in time series forecasting have found extensive applications across diverse domains, including demand forecasting [43], stock trend forecasting in financial markets [44], power load forecasting [45], traffic flow forecasting [46], energy consumption forecasting [47], and more. Statistical-based time series prediction methods are commonly employed for linear time series modeling; however, real-world research reveals nonlinear characteristics within these time series.…”