An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection
Energy demand prediction is essential in ensuring national energy security, promoting high-quality economic development, advancing sustainable development, optimizing the energy structure, and achieving dual carbon goals. In recent years, machine learning (ML) algorithms have been extensively used i...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11098871/ |
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| author | Yiwen Wang Weibin Cheng Yuting Jin Jifei Li Yantian Yang Shaobing Hu |
| author_facet | Yiwen Wang Weibin Cheng Yuting Jin Jifei Li Yantian Yang Shaobing Hu |
| author_sort | Yiwen Wang |
| collection | DOAJ |
| description | Energy demand prediction is essential in ensuring national energy security, promoting high-quality economic development, advancing sustainable development, optimizing the energy structure, and achieving dual carbon goals. In recent years, machine learning (ML) algorithms have been extensively used in energy demand prediction but with poor interpretability. This study proposes an interpretable ML framework for energy demand prediction based on the Boruta-Lasso two-stage feature selection model, extreme gradient boosting (XGBoost) regression model, grid search optimization algorithm, and Shapley additive explanations (SHAP) algorithm. Taking China as an example, the results show that the XGBoost-based model achieves R2, MAE, and RMSE of 0.9975, 0.0108, and 0.0157, respectively, with higher prediction accuracy compared to other models. In addition, the analysis based on the SHAP algorithm shows that gross national income (GNI), gross domestic product (GDP), total population at the year-end, per capita consumption expenditure, and the proportion of added value of the secondary industry are the main drivers of energy demand. The framework established in this study helps to screen influencing factors, predict energy demand, and simultaneously explain the relationship between these factors and energy demand. |
| format | Article |
| id | doaj-art-8a8da6931c3f4bfda5c563b8d87e79c4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8a8da6931c3f4bfda5c563b8d87e79c42025-08-20T03:40:17ZengIEEEIEEE Access2169-35362025-01-011313580613582110.1109/ACCESS.2025.359355811098871An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature SelectionYiwen Wang0https://orcid.org/0009-0002-6214-589XWeibin Cheng1https://orcid.org/0000-0003-4242-1404Yuting Jin2https://orcid.org/0009-0007-7787-8988Jifei Li3https://orcid.org/0009-0008-7986-8721Yantian Yang4https://orcid.org/0009-0002-0924-3503Shaobing Hu5https://orcid.org/0000-0003-2484-7145College of Geophysics and Petroleum Resources, Yangtze University, Wuhan, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, ChinaEnergy demand prediction is essential in ensuring national energy security, promoting high-quality economic development, advancing sustainable development, optimizing the energy structure, and achieving dual carbon goals. In recent years, machine learning (ML) algorithms have been extensively used in energy demand prediction but with poor interpretability. This study proposes an interpretable ML framework for energy demand prediction based on the Boruta-Lasso two-stage feature selection model, extreme gradient boosting (XGBoost) regression model, grid search optimization algorithm, and Shapley additive explanations (SHAP) algorithm. Taking China as an example, the results show that the XGBoost-based model achieves R2, MAE, and RMSE of 0.9975, 0.0108, and 0.0157, respectively, with higher prediction accuracy compared to other models. In addition, the analysis based on the SHAP algorithm shows that gross national income (GNI), gross domestic product (GDP), total population at the year-end, per capita consumption expenditure, and the proportion of added value of the secondary industry are the main drivers of energy demand. The framework established in this study helps to screen influencing factors, predict energy demand, and simultaneously explain the relationship between these factors and energy demand.https://ieeexplore.ieee.org/document/11098871/Energy demand predictioninterpretabilityfeature selectionBoruta-Lasso modelXGBoostSHAP |
| spellingShingle | Yiwen Wang Weibin Cheng Yuting Jin Jifei Li Yantian Yang Shaobing Hu An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection IEEE Access Energy demand prediction interpretability feature selection Boruta-Lasso model XGBoost SHAP |
| title | An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection |
| title_full | An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection |
| title_fullStr | An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection |
| title_full_unstemmed | An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection |
| title_short | An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection |
| title_sort | xgboost shap model for energy demand prediction with boruta x2013 lasso feature selection |
| topic | Energy demand prediction interpretability feature selection Boruta-Lasso model XGBoost SHAP |
| url | https://ieeexplore.ieee.org/document/11098871/ |
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