Carbon price forecasting with complex network and extreme learning machine

2020 ◽  
Vol 545 ◽  
pp. 122830 ◽  
Author(s):  
Hua Xu ◽  
Minggang Wang ◽  
Shumin Jiang ◽  
Weiguo Yang
Keyword(s):  
Extreme Learning Machine ◽  
Complex Network ◽  
Carbon Price ◽  
Price Forecasting ◽  
Learning Machine

scholarly journals 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

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4283 ◽  
Author(s):  
Xing Zhang ◽  
Chongchong Zhang ◽  
Zhuoqun Wei
Keyword(s):  
Singular Value Decomposition ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Singular Value ◽  
Price Forecasting ◽  
Economic Factors ◽  
Causality Test ◽  
Internal Factors ◽  
Learning Machine ◽  
Value Decomposition

Carbon price forecasting is significant to both policy makers and market participants. However, since the complex characteristics of carbon prices are affected by many factors, it may be hard for a single prediction model to obtain high-precision results. As a consequence, a new hybrid model based on multi-resolution singular value decomposition (MRSVD) and the extreme learning machine (ELM) optimized by moth–flame optimization (MFO) is proposed for carbon price prediction. First, through the augmented Dickey–Fuller test (ADF), cointegration test and Granger causality test, the external factors of the carbon price, which includes energy and economic factors, are selected in turn. To select the internal factors of the carbon price, the carbon price series are decomposed by MRSVD, and the lags are determined by partial autocorrelation function (PACF). MFO is then used for the optimization of ELM parameters, and external and internal factors are input to the MFO-ELM. Finally, to test the capability and effectiveness of the proposed model, MRSVD-MFO-ELM and its comparison models are used for carbon price forecast in the European Union (EU) and China, respectively. The results show that the performance of the model is significantly better than other models.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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

2021 ◽  
Vol 13 (9) ◽  
pp. 4896
Author(s):  
Jianguo Zhou ◽  
Dongfeng Chen
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Emission Reduction ◽  
Search Algorithm ◽  
Carbon Price ◽  
Integration Scheme ◽  
Combination Model ◽  
Price Prediction ◽  
Price Series ◽  
Learning Machine

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.

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

Energies ◽  
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.

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

Energies ◽  
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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