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An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
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Qingyao Qiao ,
Hassan Saadatmand,
Mohammad Ali Sahraei
Energy, Start page: 129499
Swansea University Author:
Hamid Eskandari
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Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy.
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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...
| Published in: | Energy |
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| ISSN: | 0360-5442 |
| Published: |
Elsevier BV
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64993 |
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| 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 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. |
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| Keywords: |
Energy consumption forecasting, CO2 emissions forecasting, Transportation sector, Machine learning, Feature selection |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Swansea University |
| Start Page: |
129499 |