Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine

2022 ◽  
Vol 156 ◽  
pp. 111783
Author(s):  
Jujie Wang ◽  
Quan Cui ◽  
Maolin He
Keyword(s):  
Extreme Learning Machine ◽  
Carbon Price ◽  
Variational Mode Decomposition ◽  
Price Prediction ◽  
Mode Decomposition ◽  
Learning Machine

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.

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 Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1375 ◽  
Author(s):  
Hui Li ◽  
Bangji Fan ◽  
Rong Jia ◽  
Fang Zhai ◽  
Liang Bai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Grey Wolf Optimizer ◽  
Mechanical Equipment ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Fault Features ◽  
Frequency Domains ◽  
Learning Machine

Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

Ultra-short-term wind speed prediction based on variational mode decomposition and optimized extreme learning machine

2021 ◽  
pp. 0309524X2110385
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Learning Machine

The main purpose of this paper is to improve the prediction accuracy of ultra-short-term wind speed. It is difficult to predict the ultra-short-term wind speed because of its unstable, non-stationary and non-linear. Aiming at the unstable and non-stationary characteristics of ultra-short-term wind speed, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed data, and a series of stable and stationary components with different frequencies are obtained. The extreme learning machine with good prediction performance and real-time performance is selected as the prediction model of decomposed components. In order to solve the problem of random setting of input weights and bias of extreme learning machine, whale optimization algorithm is used to optimize extreme learning machine to improve the regression performance. The performance of the developed prediciton model is verified by real ultra-short-term wind speed sample data. Five prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, eight performance indicators, and Pearson’s test correlation coefficient, the results show that the proposed prediction model has high prediction accuracy.

scholarly journals Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

2019 ◽  
Vol 13 (1) ◽  
pp. 4477-4492
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
L. M. Hee ◽  
Z. A. B. Ahmad
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Processing Method ◽  
Mode Number ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Learning Machine

Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.  

scholarly journals Health status identification of catenary based on VMD and FA-ELM

2021 ◽  
Vol 15 ◽  
pp. 174830262110248
Author(s):  
Lingzhi Yi ◽  
You Guo ◽  
Nian Liu ◽  
Jian Zhao ◽  
Wang Li ◽  
...  
Keyword(s):  
Health Status ◽  
Extreme Learning Machine ◽  
Firefly Algorithm ◽  
Intrinsic Mode Function ◽  
Driving Safety ◽  
Variational Mode Decomposition ◽  
Machine Model ◽  
Mode Function ◽  
Mode Decomposition ◽  
Learning Machine

Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the catenary based on firefly algorithm optimized extreme learning machine combined with variational mode decomposition is proposed in this paper. Variational mode decomposition is used to decompose the original detection curve of catenary into a series of intrinsic mode function components, and the intrinsic mode function components filtered by the correlation coefficient method after decomposing each detection curve are input into the firefly algorithm optimized extreme learning machine model to realize health status identification. Compared with some other models, the results show that the proposed model has better health status identification effect.

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