Carbon Trading Price Prediction Method Based on VMD-LSSVM

MU Huanhuan; YU Ling; YUAN Ye; GUO Haidong; WANG Xianglong; HOU Shaobo

doi:10.13296/j.1001-1609.hva.2025.12.030

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Carbon Trading Price Prediction Method Based on VMD-LSSVM

Technical Discussion | 更新时间：2025-12-09

- Carbon Trading Price Prediction Method Based on VMD-LSSVM
- High Voltage Apparatus Vol. 61, Issue 12, Pages: 219-228(2025)
- 作者机构：
  
  华电（林周）新能源有限公司，拉萨 850000
  三峡大学电气与新能源学院，湖北宜昌 443002
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2025.12.030
  CLC：
- Received：07 May 2025，
  
  Revised：2025-07-28，
  
  Published：16 December 2025
- 稿件说明：
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MU Huanhuan, YU Ling, YUAN Ye, et al. Carbon Trading Price Prediction Method Based on VMD-LSSVM[J]. High Voltage Apparatus, 2025, 61(12): 219-228.
DOI：

MU Huanhuan, YU Ling, YUAN Ye, et al. Carbon Trading Price Prediction Method Based on VMD-LSSVM[J]. High Voltage Apparatus, 2025, 61(12): 219-228. DOI： 10.13296/j.1001-1609.hva.2025.12.030.

摘要

在对碳交易价格进行预测时，如何考虑在保证预测精度的同时提高预测效率是目前亟需解决的问题。针对该问题，文中考虑到现有预测方法中的碳价序列存在模态混叠问题和预测模型求解复杂的情况。从而提出一种基于VMD-LSSVM（variational mode decomposition-least squares support vector machine）的碳交易价格预测方法。首先，利用VMD算法将历史碳价样本序列分解为频率不同的碳价分量；其次，建立LSSVM模型并对分解后的各个碳价分量进行预测；最后，叠加计算全部碳价分量的预测值得到最终的碳价预测结果。基于湖北碳排放权交易中心2017年至2019年期间的历史碳价数据仿真结果，验证了文中方法的正确性和有效性。

Abstract

In case of prediction of carbon trading price，how to improve the prediction efficiency while improving the prediction efficiency is an important problem that needs to be solved. Regarding this issue，the fact that the carbon price sequence in the existing prediction methods has the problem of modal aliasing and the solution of the prediction model is complex is taken into account in this paper.Therefore，a carbon price prediction method based on VMD-LSSVM（variational mode decomposition-least squares support vector machine）is proposed. First，the VMD algorithm is used to decompose the historical carbon price sample sequence into carbon price components with different frequencies. Then，the LSSVM model is set up and each carbon price component after decomposition is predicted. Finally，the predicted values of all carbon price components are superimposed to get the final carbon price prediction result. Based on the simulation results of the historical carbon price data of Hubei Carbon Emission Trading Center from 2017 to 2019，the correctness and effectiveness of the proposed method are demonstrated.

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Keywords

references

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