预测高峰电力负荷:具有平滑非凸ϵ不敏感损失的鲁棒支持向量回归

IF 3.4 3区 经济学 Q1 ECONOMICS
Rujia Nie, Jinxing Che, Fang Yuan, Weihua Zhao
{"title":"预测高峰电力负荷:具有平滑非凸ϵ不敏感损失的鲁棒支持向量回归","authors":"Rujia Nie,&nbsp;Jinxing Che,&nbsp;Fang Yuan,&nbsp;Weihua Zhao","doi":"10.1002/for.3118","DOIUrl":null,"url":null,"abstract":"<p>Peak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non-convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave-convex programming algorithm is used to solve the non-convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non-convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non-convex support vector regression in robustness and generalization ability.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting peak electric load: Robust support vector regression with smooth nonconvex ϵ-insensitive loss\",\"authors\":\"Rujia Nie,&nbsp;Jinxing Che,&nbsp;Fang Yuan,&nbsp;Weihua Zhao\",\"doi\":\"10.1002/for.3118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Peak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non-convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave-convex programming algorithm is used to solve the non-convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non-convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non-convex support vector regression in robustness and generalization ability.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3118\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3118","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

摘要

高峰电力负荷预测是能源行业商业运营的关键部分。虽然各种负荷预测方法和技术已被提出并在实践中得到检验,但对异常事故的容忍度这一日益增长的课题是开发鲁棒的高峰负荷预测模型。本文提出了一种鲁棒平滑非凸支持向量回归方法,通过调整自适应控制损失值和自适应鲁棒参数,降低异常值或噪声对决策函数的负面影响,从而提高模型的鲁棒性。凹凸编程算法用于解决优化问题的非凸性。线性回归模型和非线性回归模型以及两个真实数据集都取得了良好的结果。在中国江西省的一家电力公司进行了实验,以评估鲁棒平滑非凸支持向量回归模型的性能。结果表明,所提出的方法在鲁棒性和泛化能力方面优于支持向量回归和广义二次非凸支持向量回归。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting peak electric load: Robust support vector regression with smooth nonconvex ϵ-insensitive loss

Peak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non-convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave-convex programming algorithm is used to solve the non-convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non-convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non-convex support vector regression in robustness and generalization ability.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信