Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy

IF 2.6 3区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY Nuclear Engineering and Technology Pub Date : 2024-08-17 DOI:10.1016/j.net.2024.08.013
Anjali Nighoskar, Preeti Chaurasia, Nagendra Singh
{"title":"Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy","authors":"Anjali Nighoskar, Preeti Chaurasia, Nagendra Singh","doi":"10.1016/j.net.2024.08.013","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":19272,"journal":{"name":"Nuclear Engineering and Technology","volume":"77 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Engineering and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.net.2024.08.013","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
推进核能预测:探索回归建模技术以提高准确性
对可持续和可靠能源的迫切需求激发了人们对精确预测核能发电量的更大兴趣。这项研究利用复杂的回归建模方法(即 XGBoost),通过国内生产总值 (GDP) 等经济指标来预测核能发电量。通过研究各地核能产量和 GDP 的历史数据,对每个模型的预测准确性进行了评估。这里使用了均方误差 (MSE) 和判定系数 (R2) 等指标来分析其有效性。研究结果表明,XGBoost 模型优于标准回归方法,显示出更大的 R2 值和更低的 MSE 分数。此外,这些发现对能源政策制定的影响也为今后的能源预测研究提供了可能的方向。这项研究为能源规划者和决策者提供了有用的见解,使他们能够更深刻地理解经济指标与核能发电之间的复杂关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Nuclear Engineering and Technology
Nuclear Engineering and Technology 工程技术-核科学技术
CiteScore
4.80
自引率
7.40%
发文量
431
审稿时长
3.5 months
期刊介绍: Nuclear Engineering and Technology (NET), an international journal of the Korean Nuclear Society (KNS), publishes peer-reviewed papers on original research, ideas and developments in all areas of the field of nuclear science and technology. NET bimonthly publishes original articles, reviews, and technical notes. The journal is listed in the Science Citation Index Expanded (SCIE) of Thomson Reuters. NET covers all fields for peaceful utilization of nuclear energy and radiation as follows: 1) Reactor Physics 2) Thermal Hydraulics 3) Nuclear Safety 4) Nuclear I&C 5) Nuclear Physics, Fusion, and Laser Technology 6) Nuclear Fuel Cycle and Radioactive Waste Management 7) Nuclear Fuel and Reactor Materials 8) Radiation Application 9) Radiation Protection 10) Nuclear Structural Analysis and Plant Management & Maintenance 11) Nuclear Policy, Economics, and Human Resource Development
期刊最新文献
The impact of pressure rate on the physical, structural and gamma-ray shielding capabilities of novel light-weight clay bricks Towards accurate dose assessment for emergency industrial radiography source retrieval operations: A preliminary study of 4D Monte Carlo dose calculations Technical reviewers for Nuclear Engineering and Technology, 2024 An innovative and efficient implementation of matrix-free Newton krylov method for neutronics/thermal-hydraulics coupling simulation Numerical investigation of welding deformation diminution for double shell structure using the layered inherent strain
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1