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P.V. Matrenin, A.G. Rusina, "Operational forecasting of railway station electrical energy consumption based on the analysis of consumption data of nearby stations", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 24.2, 2022, doi:10.4203/ccc.1.24.2

Keywords: railway electrification, forecasting, data analysis, energy consumption.

Abstract

This paper deals with the problem of operational forecasting of railway station electrical energy consumption. The relevance of the task lies in the fact that for the effective integration of renewable energy sources into the power supply system of railway stations, it is necessary to use energy storage systems and understand at what moments energy needs to be stored and when to spend. To control the energy storage system, it is essential to predict the processes of both generation and consumption. The complex aperiodic schedule of railway station energy consumption makes it difficult to use forecasting methods that are successfully used for other objects in the power industry, such as autoregressive models. At the same time, each station is an element of a whole system of railway stations connected by the train traffic. Therefore, the study tested the hypothesis that the station’s electrical energy consumption forecasting can be obtained by analysing the consumption of other stations one hour before the forecast. The obtained results of building a regression model on real-life data collected from 36 stations are shown. Forecast error (mean absolute percentage error) is 19%, which, given the flexibility that the energy storage system brings to the station’s power system, is an acceptable result.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 1 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 24.2 Operational forecasting of railway station electrical energy consumption based on the analysis of consumption data of nearby stations P.V. Matrenin¹ and A.G. Rusina² ¹Industry Power Supply Systems Department, Novosibirsk State Technical University, Novosibirsk, Russia ²Power Stations Department, Novosibirsk State Technical University, Novosibirsk, Russia doi:10.4203/ccc.1.24.2 Full Bibliographic Reference for this paper P.V. Matrenin, A.G. Rusina, "Operational forecasting of railway station electrical energy consumption based on the analysis of consumption data of nearby stations", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 24.2, 2022, doi:10.4203/ccc.1.24.2 Keywords: railway electrification, forecasting, data analysis, energy consumption. Abstract This paper deals with the problem of operational forecasting of railway station electrical energy consumption. The relevance of the task lies in the fact that for the effective integration of renewable energy sources into the power supply system of railway stations, it is necessary to use energy storage systems and understand at what moments energy needs to be stored and when to spend. To control the energy storage system, it is essential to predict the processes of both generation and consumption. The complex aperiodic schedule of railway station energy consumption makes it difficult to use forecasting methods that are successfully used for other objects in the power industry, such as autoregressive models. At the same time, each station is an element of a whole system of railway stations connected by the train traffic. Therefore, the study tested the hypothesis that the station’s electrical energy consumption forecasting can be obtained by analysing the consumption of other stations one hour before the forecast. The obtained results of building a regression model on real-life data collected from 36 stations are shown. Forecast error (mean absolute percentage error) is 19%, which, given the flexibility that the energy storage system brings to the station’s power system, is an acceptable result. download the full-text of this paper (PDF, 6 pages, 486 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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