Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors

Zhang, Xing; Zhang, Chongchong; Wei, Zhuoqun

doi:10.3390/en12224283

Cited by 17 publications

(10 citation statements)

References 37 publications

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“…Zhang et al decomposed the original data series by multi-resolution singular value decomposition method and forecasted the coal price by using MFO-optimized ELM model. Experimental results show the forecasting performance of the proposed model was superior to that of the contrast model (Zhang et al, 2019). However, the neural network model is a black box model which is difficult to interpret.…”

Section: Introductionmentioning

confidence: 93%

Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition

Tang

et al. 2021

Front. Energy Res.

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Aiming at the problem of difficulties in modeling the nonlinear relation in the steam coal dataset, this article proposes a forecasting method for the price of steam coal based on robust regularized kernel regression and empirical mode decomposition. By selecting the polynomial kernel function, the robust loss function and L2 regular term to construct a robust regularized kernel regression model are used. The polynomial kernel function does not depend on the kernel parameters and can mine the global rules in the dataset so that improves the forecasting stability of the kernel model. This method maps the features to the high-dimensional space by using the polynomial kernel function to transform the nonlinear law in the original feature space into linear law in the high-dimensional space and helps learn the linear law in the high-dimensional feature space by using the linear model. The Huber loss function is selected to reduce the influence of abnormal noise in the dataset on the model performance, and the L2 regular term is used to reduce the risk of model overfitting. We use the combined model based on empirical mode decomposition (EMD) and auto regressive integrated moving average (ARIMA) model to compensate for the error of robust regularized kernel regression model, thus making up for the limitations of the single forecasting model. Finally, we use the steam coal dataset to verify the proposed model and such model has an optimal evaluation index value compared to other contrast models after the model performance is evaluated as per the evaluation index such as RMSE, MAE, and mean absolute percentage error.

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Section: Introductionmentioning

confidence: 93%

Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition

Tang

et al. 2021

Front. Energy Res.

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“…Supply and demand components, such as number of distributed allowances or expected emissions, can usually be expected to define the CO 2 price, but the allowance market can also be affected by macroeconomic or financial market shocks [9]. Several authors [10][11][12][13][14][15] have analyzed the relationship between EUA prices and their fundamentals (prices of gas, coal, oil...) or the link between CO 2 prices and the stock market returns of the power sector (one of the most polluting industries). They concluded that, in the short run, EUAs evolve like financial assets due to the time delay in their response to changes in fuel prices or to a new technical situation.…”

Section: Forecasting Modelmentioning

confidence: 99%

“…It is generally accepted that economic variables behave like nonlinear processes [16]. Non-linearity is rather troublesome when modeling the dynamics of time series describing the evolution of such economic variables [12,14,15,17]. To overcome this issue, several methods have been proposed in the literature [10,17].…”

Section: Forecasting Modelmentioning

confidence: 99%

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Jaramillo-Morán

García-García

2019

Energies

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In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

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“…It is the world's first multinational emissions trading system, based on cap-and-trade, which provides a way to reduce emissions at the lowest economic cost. Carbon emissions can be effectively reduced by buying and selling carbon permits (Zhang et al, 2019). China is the world's second-largest emitter of greenhouse gases and is seen by many countries as the most promising market for reducing emissions, even though it is not constrained to do so.…”

Section: Introductionmentioning

confidence: 99%

“…(Sun and Huang, 2020) used variational mode decomposition (VMD) to decompose the first inherent mode function (IMF1), and improved the prediction effect through secondary decomposition. In (Zhang et al, 2019)' research, the carbon price is decomposed into approximate sequence and detailed sequence after the high-frequency components of previous carbon price data is eliminated by the multi-resolution singular value decomposition method, and the two-time series were used as the prediction input.…”

Section: Introductionmentioning

confidence: 99%

Optimized Extreme Learning Machine (ELM) Based on Genetic Algorithm (GA) To Predict Carbon Price Under The Influence of Multiple Factors

Sun

Ren

2021

Preprint

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The promotion of carbon market can accelerate the pace of low-carbon transformation of China's economic structure and achieve more efficient carbon emission reduction. Accurate carbon price prediction is conducive to improving the risk management of carbon market and the decision-making of investors, but it also brings great challenges to relevant industry practitioners and the government. In this paper, a new hybrid model is proposed, which combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and genetic algorithm (GA) optimized extreme learning machine (ELM). The application of GA-ELM in carbon price prediction is firstly studied in this paper. Eight intrinsic mode functions and one residual can be obtained by CEEMDAN, and then partial autocorrelation (PACF) is used to determine the partial correlation between each sequence and its lag data, and they were taken as internal factors affecting the prediction. At the same time, energy, economic and social factors are selected as the external factors affecting the prediction, and the carbon price prediction is realized through internal and external factors. It has been proved that the model successfully overcomes the challenge of carbon price prediction based on multiple influencing factors. The hybrid model shows superiority in Beijing, Shanghai and Guangdong. The results show that the prediction performance of the proposed model is the best among the 15 models, and the prediction accuracy will be improved due to the decomposition of the carbon price. Besides, the CEEMDAN-GA-ELM model better overcomes the challenge of carbon price prediction with multiple influencing factors. This model provides a novel and effective tool for the government and enterprises to predict the carbon price.

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Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors

Cited by 17 publications

References 37 publications

Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition

Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Optimized Extreme Learning Machine (ELM) Based on Genetic Algorithm (GA) To Predict Carbon Price Under The Influence of Multiple Factors

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