Carbon price prediction under output uncertainty

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

Energies ◽

10.3390/en14051328 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1328

Author(s):

Jianguo Zhou ◽

Shiguo Wang

Keyword(s):

Prediction Model ◽

Extreme Learning Machine ◽

Influencing Factors ◽

Search Algorithm ◽

Carbon Price ◽

Carbon Trading ◽

Price Prediction ◽

Model Based ◽

Mode Decomposition ◽

Learning Machine

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.

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A risk analysis method for carbon price prediction with hybrid intelligent model in consideration of variable selection of graphical modeling

2008 IEEE International Conference on Sustainable Energy Technologies ◽

10.1109/icset.2008.4747156 ◽

2008 ◽

Cited By ~ 2

Author(s):

Hiroyuki Mori ◽

Wenjun Jiang

Keyword(s):

Risk Analysis ◽

Variable Selection ◽

Carbon Price ◽

Analysis Method ◽

Graphical Modeling ◽

Price Prediction ◽

Intelligent Model ◽

Selection Of

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A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network

Journal of Cleaner Production ◽

10.1016/j.jclepro.2019.118671 ◽

2020 ◽

Vol 243 ◽

pp. 118671 ◽

Cited By ~ 16

Author(s):

Wei Sun ◽

Chenchen Huang

Keyword(s):

Neural Network ◽

Prediction Model ◽

Back Propagation ◽

Carbon Price ◽

Back Propagation Neural Network ◽

Decomposition Algorithm ◽

Price Prediction ◽

Model Based

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Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

Energies ◽

10.3390/en13133471 ◽

2020 ◽

Vol 13 (13) ◽

pp. 3471

Author(s):

Wei Sun ◽

Junjian Zhang

Keyword(s):

Extreme Learning Machine ◽

Empirical Mode Decomposition ◽

Bat Algorithm ◽

Carbon Price ◽

Ensemble Empirical Mode Decomposition ◽

Carbon Market ◽

Energy Price ◽

Price Prediction ◽

Mode Decomposition ◽

Learning Machine

In response to climate change and environmental issues, many countries have gradually optimized carbon market management and improved the carbon market trading mechanism. Carbon price prediction plays a pivotal role in promoting carbon market management when investors are guided by prediction to conduct rational carbon trading. A novel carbon price prediction methodology is constructed based on ensemble empirical mode decomposition, improved bat algorithm, and extreme learning machine (EEMD-IBA-ELM) in this study. Firstly, the carbon price is decomposed into multiple regular intrinsic mode function (IMF) components by the ensemble empirical mode decomposition, and partial autocorrelation analysis (PACF) is used to find IMF historical data affecting the current value of IMF. Secondly, the improved bat algorithm (IBA) is used to heighten extreme learning machine (ELM) while adaptive parameters are obtained. Finally, EEMD-IBA-ELM was established to predict carbon price. Simultaneously, energy price fluctuation is introduced into the carbon price prediction model. As a consequence, EEMD-IBA-ELM carbon price prediction ability is further improved. In the empirical analysis, the historical carbon price of European Climate Exchange (ECX) and Korea Exchange (KRX) markets are used to examine the effectiveness and stability of the model. Errors of carbon price prediction in ECX and KRX is 2.1982% and 1.1762%, respectively. The results show that the EEMD-IBA-ELM carbon price prediction model can accurately predict carbon price when prediction effect shows strong stability. Furthermore, carbon price prediction accurateness was significantly enhanced by using energy price fluctuation as an influencing factor of carbon price prediction.

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An Ensemble Prediction System Based on Artificial Neural Networks and Deep Learning Methods for Deterministic and Probabilistic Carbon Price Forecasting

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.740093 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yi Yang ◽

Honggang Guo ◽

Yu Jin ◽

Aiyi Song

Keyword(s):

Feature Extraction ◽

Prediction Error ◽

Prediction Interval ◽

Carbon Price ◽

Ensemble Prediction ◽

Price Forecasting ◽

Prediction System ◽

Ensemble Prediction System ◽

Price Prediction ◽

Carbon Prices

Carbon price prediction is important for decreasing greenhouse gas emissions and coping with climate change. At present, a variety of models are widely used to predict irregular, nonlinear, and nonstationary carbon price series. However, these models ignore the importance of feature extraction and the inherent defects of using a single model; thus, accurate and stable prediction of carbon prices by relevant industry practitioners and the government is still a huge challenge. This research proposes an ensemble prediction system (EPS) that includes improved data feature extraction technology, three prediction submodels (GBiLSTM, CNN, and ELM), and a multiobjective optimization algorithm weighting strategy. At the same time, based on the best fitting distribution of the prediction error of the EPS, the carbon price prediction interval is constructed as a way to explore its uncertainty. More specifically, EPS integrates the advantages of various submodels and provides more accurate point prediction results; the distribution function based on point prediction error is used to establish the prediction interval of carbon prices and to mine and analyze the volatility characteristics of carbon prices. Numerical simulation of the historical data available for three carbon price markets is also conducted. The experimental results show that the ensemble prediction system can provide more effective and stable carbon price forecasting information and that it can provide valuable suggestions that enterprise managers and governments can use to improve the carbon price market.

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