Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy
The urgent requirement for sustainable and dependable energy sources has stimulated an increased fascination with precisely forecasting nuclear energy generation. This work utilizes sophisticated regression modeling approaches, namely XGBoost, to predict nuclear energy generation by leveraging econo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Nuclear Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573324003917 |
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| author | Anjali Nighoskar Preeti Chaurasia Nagendra Singh |
| author_facet | Anjali Nighoskar Preeti Chaurasia Nagendra Singh |
| author_sort | Anjali Nighoskar |
| collection | DOAJ |
| description | The urgent requirement for sustainable and dependable energy sources has stimulated an increased fascination with precisely forecasting nuclear energy generation. This work utilizes sophisticated regression modeling approaches, namely XGBoost, to predict nuclear energy generation by leveraging economic indices such as Gross Domestic Product (GDP). Each model's prediction accuracy has been evaluated by examining historical data on nuclear energy output and GDP from various locations. Here, measures such as mean squared error (MSE) and coefficient of determination (R2) to analyze their effectiveness have been used. The results of this study demonstrate that the XGBoost model outperforms standard regression approaches, showing greater R2 values and lower MSE scores. Furthermore, the consequences of these discoveries for the development of energy policy offer possible directions for future study in energy forecasting. This study provides useful insights for energy planners and policymakers, enabling a more profound comprehension of the complex relationship between economic indicators and nuclear energy generation. |
| format | Article |
| id | doaj-art-108d045f78954db59c1ac8e80a02ef5c |
| institution | Kabale University |
| issn | 1738-5733 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj-art-108d045f78954db59c1ac8e80a02ef5c2025-01-12T05:24:37ZengElsevierNuclear Engineering and Technology1738-57332025-01-01571103144Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracyAnjali Nighoskar0Preeti Chaurasia1Nagendra Singh2School of Engineering and Technology, Jagran Lakecity University, Bhopal, IndiaBansal Institute of Science and Technology, Bhopal, IndiaDepartment of Electrical Engineering, Trinity College of Engineering and Technology, Telangana, India; Corresponding author.The urgent requirement for sustainable and dependable energy sources has stimulated an increased fascination with precisely forecasting nuclear energy generation. This work utilizes sophisticated regression modeling approaches, namely XGBoost, to predict nuclear energy generation by leveraging economic indices such as Gross Domestic Product (GDP). Each model's prediction accuracy has been evaluated by examining historical data on nuclear energy output and GDP from various locations. Here, measures such as mean squared error (MSE) and coefficient of determination (R2) to analyze their effectiveness have been used. The results of this study demonstrate that the XGBoost model outperforms standard regression approaches, showing greater R2 values and lower MSE scores. Furthermore, the consequences of these discoveries for the development of energy policy offer possible directions for future study in energy forecasting. This study provides useful insights for energy planners and policymakers, enabling a more profound comprehension of the complex relationship between economic indicators and nuclear energy generation.http://www.sciencedirect.com/science/article/pii/S1738573324003917EnergyRenewable energyNon-renewable energyNuclear energySolar energyXGBoost models |
| spellingShingle | Anjali Nighoskar Preeti Chaurasia Nagendra Singh Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy Nuclear Engineering and Technology Energy Renewable energy Non-renewable energy Nuclear energy Solar energy XGBoost models |
| title | Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy |
| title_full | Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy |
| title_fullStr | Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy |
| title_full_unstemmed | Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy |
| title_short | Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy |
| title_sort | advancing nuclear energy forecasting exploring regression modeling techniques for improved accuracy |
| topic | Energy Renewable energy Non-renewable energy Nuclear energy Solar energy XGBoost models |
| url | http://www.sciencedirect.com/science/article/pii/S1738573324003917 |
| work_keys_str_mv | AT anjalinighoskar advancingnuclearenergyforecastingexploringregressionmodelingtechniquesforimprovedaccuracy AT preetichaurasia advancingnuclearenergyforecastingexploringregressionmodelingtechniquesforimprovedaccuracy AT nagendrasingh advancingnuclearenergyforecastingexploringregressionmodelingtechniquesforimprovedaccuracy |