A novel machine learning-based electricity price forecasting model based on optimal model selection strategy
文献类型：期刊论文
| 作者 | Yang, Wendong1,2; Sun, Shaolong3; Hao, Yan4; Wang, Shouyang5,6,7 |
| 刊名 | ENERGY
 |
| 出版日期 | 2022 |
| 卷号 | 238页码:14 |
| ISSN号 | 0360-5442 |
| 关键词 | Electricity price Forecasting Hybrid model Model selection Kernel-based extreme learning machine |
| DOI | 10.1016/j.energy.2021.121989 |
| 英文摘要 | Current electricity price forecasting models rely on only simple hybridizations of data preprocessing and optimization methods while ignoring the significance of adaptive data preprocessing and effective optimization and selection strategies to obtain optimal models that improve the forecasting performance. To solve these problems, this study develops an improved electricity price forecasting model that offers the advantages of adaptive data preprocessing, advanced optimization method, kernel-based model, and optimal model selection strategy. Specifically, the adaptive parameter-based variational mode decomposition technology is proposed to provide desirable data preprocessing results, and a leave-one-out optimization strategy based on the chaotic sine cosine algorithm is proposed and applied to develop optimal kernel-based extreme learning machine models. In addition, a newly proposed optimal model selection strategy is applied to determine the developed model that provides the most desirable forecasting result. Numerical results show that the developed model's performance metrics were best, and the average values of mean absolute error, root mean square error, mean absolute percentage error, index of agreement, and Theil's inequality coefficient in four datasets are 0.5121, 0.7607, 0.5722%, 0.9997 and 0.0041, respectively, which imply that the developed model is a promising, applicable and effective electricity price forecasting technique in the real electricity market. (c) 2021 Elsevier Ltd. All rights reserved. |
| 资助项目 | National Natural Science Founda-tion of China[71988101] ; National Natural Science Founda-tion of China[72101197] ; National Natural Science Founda-tion of China[72101138] ; Humanities and Social Science Fund of Ministry of Ed-ucation of the People's Republic of China[21YJCZH198] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:000702790700012 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/59326]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Sun, Shaolong |
| 作者单位 | 1.Shandong Univ Finance & Econ, Sch Management Sci & Engn, Jinan 250014, Shandong, Peoples R China 2.Shandong Univ Finance & Econ, Inst Marine Econ & Management, Jinan, Shandong, Peoples R China 3.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Shaanxi, Peoples R China 4.Shandong Normal Univ, Business Sch, Jinan 250014, Shandong, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 7.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Wendong,Sun, Shaolong,Hao, Yan,et al. A novel machine learning-based electricity price forecasting model based on optimal model selection strategy[J]. ENERGY,2022,238:14. |
| APA | Yang, Wendong,Sun, Shaolong,Hao, Yan,&Wang, Shouyang.(2022).A novel machine learning-based electricity price forecasting model based on optimal model selection strategy.ENERGY,238,14. |
| MLA | Yang, Wendong,et al."A novel machine learning-based electricity price forecasting model based on optimal model selection strategy".ENERGY 238(2022):14. |
入库方式： OAI收割
来源：数学与系统科学研究院
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