Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Zhou, Jianguo; Huo, Xuejing; Xu, Xiao-lei; Li, Yushuo

doi:10.3390/en12050950

Cited by 36 publications

(19 citation statements)

References 57 publications

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“…Sun et al [17] applied the particle swarm optimization the extreme value learning machine for carbon emission prediction research. Zhou et al [27] used Grey Wolf Optimizer (GWO) binding to the ELM model to predict carbon prices, which indicated that the predictive precision of the optimized ELM model outperformed the single ELM. Sun and Zhang [15] applied the extreme learning machine optimized by the adaptive whale optimization algorithm to carbon price prediction, and the results verified that the optimized ELM performance is more accurate than before the optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Zhou

Chen

2021

Sustainability

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Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

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Section: Literature Reviewmentioning

confidence: 99%

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Zhou

Chen

2021

Sustainability

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“…Zhang et al (2019) used ELM to predict apoptosis protein subcellular localization. Zhou et al (2019) used ELM to forecast the carbon price. Zhu et al (2019) predicted the daily water temperature for rivers by the ELM method.…”

Section: The Elm Modelmentioning

confidence: 99%

Soft sensor hybrid model of dynamic liquid level for sucker rod pump oil wells

Chen

Gao

2021

Transactions of the Institute of Measurement and Control

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The measurement of the dynamic liquid level is of great importance during the oil-producing process of rod pumping wells. It may affect the production of oil fields and motor efficiency. However, the dynamic liquid level is difficult to detect because of the circumstances in the downhole of rod pump wells. In this paper. Firstly, according to the working principles of the sucker rod pump, the mechanical models of the pumping motor and four-bar linkage mechanism are respectively built. Secondly, for the underground frictions, a mechanical model based on the energy conservation equation is built and then the mechanism model is built between the dynamic liquid level and power of the motor. To improve the accuracy of the mechanism model, a novel method based on an artificial fish swarm algorithm optimization Gaborc-kernel extreme learning machine is used to establish a soft sensor dynamic liquid level error compensation model. The mechanism model is paralleled with the soft sensor model to establish a hybrid model of dynamic liquid level. Eventually, the AFSA-Gaborc-KELM soft sensor hybrid model is verified by using the oil dataset collected from the electrical parameter acquisition equipment. This hybrid model is compared with some other models. In the comparison, the proposed hybrid model has better performance and prediction accuracy for the dynamic liquid level than the BP hybrid model, GA-ELM hybrid model, and LSSVM hybrid model.

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“…The gradient descent strategy used in the training of traditional neural networks may cause defects such as slow convergence, the disappearance of gradients, and sensitivity to parameter settings. The unique design structure of ELM gives it the advantages of fast convergence, fewer parameters to be adjusted, and strong generalization ability, which makes it widely used in research in various fields [34].…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine

Zhou

Wang

2021

Sustainability

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Carbon trading is a significant mechanism created to control carbon emissions, and the increasing enthusiasm for participation in the carbon trading market has forced the emergence of higher-precision carbon price prediction models. Facing the complexity of carbon price time series, this paper proposes a carbon price forecasting hybrid model based on secondary decomposition and an improved extreme learning machine (ELM). First, the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the carbon price several intrinsic modal functions to initially weaken the non-linearity of the original carbon price data. Secondly, the first intrinsic mode function (IMF1) with the strongest volatility is processed by the variational mode decomposition (VMD). Then, the partial autocorrelation function (PACF) is applied to obtain the model input variables for subsequences. Finally, the ELM improved by the bald eagle search (BES) algorithm is utilized to make predictions. In the empirical analysis, five actual datasets from three carbon markets are used to verify the prediction performance of the proposed model. Based on the six evaluation indicators of the predicted results, the proposed model is the best performer among all models, which suggests that CEEMDAN-VMD-BES-ELM is effective and stable in predicting carbon price.

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Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Cited by 36 publications

References 57 publications

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Soft sensor hybrid model of dynamic liquid level for sucker rod pump oil wells

Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine

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