Enhancing Energy Management in Railway Transportation - A High-Accuracy Prediction Approach Using Ensemble Machine Learning

Aug 3, 2026

Predicting railway energy consumption supports national energy management, economic development, energy security, environmental sustainability, and infrastructure investment decisions. This study forecasts Türkiye’s railway energy consumption using features including railway line length, number of passengers, freight amount, and historical energy consumption from 1977 to 2024. Energy prediction values are produced from 18 machine learning methods, then improved through ensemble learning via bagging, boosting, stacking, and blending. Results increase R-squared up to 0.9667, enabling more efficient investment planning and more effective sustainable energy strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Energy consumption forecasting and sustainability

Energy policy drivers and planning needs

Social and political dimensions of energy planning

University of Birmingham Enhancing Energy Management in Railway Transportation Kuşkapan, Emre; Çodur, Muhammed Yasin; Çodur, Merve Kayacı; Dissanayake, Dilum DOI: 10.1002/ese3.70426 License: Creative Commons: Attribution (CC BY) Document Version Publisher's PDF, also known as Version of record Citation for published version (Harvard): Kuşkapan, E, Çodur, MY, Çodur, MK & Dissanayake, D 2026, 'Enhancing Energy Management in Railway Transportation: A High‐Accuracy Prediction Approach Using Ensemble Machine Learning', Energy Science & Engineering, vol. 14, no. 1, pp. 557-567. [https://doi.org/10.1002/ese3.70426](https://doi.org/10.1002/ese3.70426) Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 07. Mar. 2026 Energy Science & Engineering ORIGINAL ARTICLE Enhancing Energy Management in Railway Transportation: A High‐Accuracy Prediction Approach Using Ensemble Machine Learning Emre Kuşkapan1 | Muhammed Yasin Çodur2 | Merve Kayacı Çodur3 | Dilum Dissanayake4 1Department of Civil Engineering, Erzurum Technical University, Erzurum, Turkey | 2College of Engineering and Technology, American University of the Middle East, Kuwait | 3Department of Industrial Engineering, Erzurum Technical University, Erzurum, Turkey | 4School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK Correspondence: Dilum Dissanayake ([d.dissanayake@bham.ac.uk](d.dissanayake@bham.ac.uk)) | Emre Kuşkapan ([emre.kuskapan@erzurum.edu.tr](emre.kuskapan@erzurum.edu.tr)) Received: 31 July 2025 | Revised: 26 September 2025 | Accepted: 16 December 2025 Keywords: energy demand | ensemble machine learning | railway transportation | sustainability ABSTRACT Predicting energy consumption helps countries make strategic decisions in many critical areas such as energy management, economic development, energy security, environmental sustainability and infrastructure investments. Therefore, accurate and reliable energy consumption predictions are vital to ensure the sustainability and prosperity of countries. This study aims to contribute to the proper planning of transportation policies and energy management by successfully predicting Türkiye's railway energy consumption. In this direction, energy prediction values were obtained from 18 different machine learning methods using the country's railway line length, number of passengers, freight amount and energy consumption values from 1977 to 2024. To further strengthen the results obtained with these metho

FAQs

What is the document’s main objective?

To predict Türkiye’s railway energy consumption accurately to support transportation policy planning and energy management.

Which data features and time range are used for prediction?

The approach uses railway line length, number of passengers, freight amount, and historical energy consumption from 1977 to 2024.

How does the study improve prediction accuracy beyond traditional machine learning?

It combines predictions from 18 machine learning methods and strengthens them using ensemble learning techniques such as bagging, boosting, stacking, and blending.

Aug 3, 2026

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