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An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

Qingyao Qiao Orcid Logo, Hamid Eskandari Orcid Logo, Hassan Saadatmand, Mohammad Ali Sahraei Orcid Logo

Energy, Volume: 286

Swansea University Author: Hamid Eskandari Orcid Logo

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DOI (Published version): 10.1016/j.energy.2023.129499

Abstract

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport...

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Published in: Energy
ISSN: 0360-5442
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa64993
first_indexed 2023-11-15T15:17:08Z
last_indexed 2024-11-25T14:15:09Z
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spelling 2024-04-11T17:17:04.3639051 v2 64993 2023-11-15 An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector d2a47b056b55373889a9d19d2924f634 0000-0002-5515-9399 Hamid Eskandari Hamid Eskandari true false 2023-11-15 CBAE The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment. Journal Article Energy 286 Elsevier BV 0360-5442 Energy consumption forecasting, CO2 emissions forecasting, Transportation sector, Machine learning, Feature selection 1 1 2024 2024-01-01 10.1016/j.energy.2023.129499 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-04-11T17:17:04.3639051 2023-11-15T15:14:17.0597772 Faculty of Humanities and Social Sciences School of Management - Business Management Qingyao Qiao 0000-0002-5541-2681 1 Hamid Eskandari 0000-0002-5515-9399 2 Hassan Saadatmand 3 Mohammad Ali Sahraei 0000-0002-9130-3685 4 64993__29671__b669c5c22be14d58921d04dcec2575c7.pdf 64993_VoR.pdf 2024-03-08T13:57:23.0204149 Output 9734984 application/pdf Version of Record true ©2023 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
spellingShingle An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
Hamid Eskandari
title_short An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
title_full An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
title_fullStr An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
title_full_unstemmed An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
title_sort An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
author_id_str_mv d2a47b056b55373889a9d19d2924f634
author_id_fullname_str_mv d2a47b056b55373889a9d19d2924f634_***_Hamid Eskandari
author Hamid Eskandari
author2 Qingyao Qiao
Hamid Eskandari
Hassan Saadatmand
Mohammad Ali Sahraei
format Journal article
container_title Energy
container_volume 286
publishDate 2024
institution Swansea University
issn 0360-5442
doi_str_mv 10.1016/j.energy.2023.129499
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment.
published_date 2024-01-01T08:26:09Z
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score 11.095469

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