基于惩罚性时态融合变压器的概率电价预测

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-02-20 DOI:10.1002/for.3084
He Jiang, Sheng Pan, Yao Dong, Jianzhou Wang
{"title":"基于惩罚性时态融合变压器的概率电价预测","authors":"He Jiang,&nbsp;Sheng Pan,&nbsp;Yao Dong,&nbsp;Jianzhou Wang","doi":"10.1002/for.3084","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high-frequent electricity price for market decision-making. However, the uncertainties associated with electricity prices, such as non-stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better-informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi-step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real-data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real-world applications.</p>\n </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probabilistic electricity price forecasting based on penalized temporal fusion transformer\",\"authors\":\"He Jiang,&nbsp;Sheng Pan,&nbsp;Yao Dong,&nbsp;Jianzhou Wang\",\"doi\":\"10.1002/for.3084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high-frequent electricity price for market decision-making. However, the uncertainties associated with electricity prices, such as non-stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better-informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi-step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real-data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real-world applications.</p>\\n </div>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-02-20\",\"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.3084\",\"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.3084","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

在放松管制的电力市场中,准确预测波动、非线性和高频率的电价对市场决策越来越重要。然而,与电价相关的不确定性,如非平稳性、非线性和高波动性,给电价预测(EPF)带来了严重困难。点预测只能提供对未来价格的单一、确定性估计,而概率预测则不同,它能更全面、更细致地反映未来的价格动态,从而帮助市场参与者在面临不确定性时做出更明智的决策。因此,在本文中,我们提出了一种用于多步骤概率预测的稳健深度学习方法。首先,我们在专家模型中使用最小绝对收缩和选择算子(LASSO)来生成点预测。其次,我们在时态融合变换器中引入了平滑剪切绝对偏差正则化项,这是一种非凸惩罚,在模型选择方面具有公认的神谕特性。最后,我们利用提出的模型整合点预测,给出概率预测。为了评估所提出的预测模型,我们在 Nord Pool 电力市场和波兰电力交易市场进行了真实数据实验。实证结果表明,与其他竞争者相比,所提出的模型具有卓越的概率预测性能,并在实际应用中证明了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Probabilistic electricity price forecasting based on penalized temporal fusion transformer

In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high-frequent electricity price for market decision-making. However, the uncertainties associated with electricity prices, such as non-stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better-informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi-step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real-data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real-world applications.

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.
期刊最新文献
Issue Information Issue Information Predictor Preselection for Mixed‐Frequency Dynamic Factor Models: A Simulation Study With an Empirical Application to GDP Nowcasting Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning Demand Forecasting New Fashion Products: A Review Paper
×
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