A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparro...

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Bibliographic Details
Published in:Energies (Basel) 2021-03, Vol.14 (5), p.1328
Main Authors: Zhou, Jianguo, Wang, Shiguo
Format: Article
Language:English
Subjects:
Algorithms
Bandwidths
Carbon dioxide
carbon price
Crude oil
Decomposition
Emissions
Emissions control
Emissions trading
empirical mode decomposition
Global economy
Global warming
kernel extreme learning machine
Learning algorithms
Neural networks
Prediction models
Prices
Search algorithms
secondary decomposition
sparrow search algorithm
Stochastic models
Time series
variational mode decomposition
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Summary:Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14051328