scholarly journals Carbon price combination prediction model based on improved variational mode decomposition

2022 ◽  
Vol 8 ◽  
pp. 1644-1664
Author(s):  
Guohui Li ◽  
Caifeng Zheng ◽  
Hong Yang
Keyword(s):  
Prediction Model ◽  
Carbon Price ◽  
Variational Mode Decomposition ◽  
Model Based ◽  
Mode Decomposition

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 A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

Energies ◽  
2016 ◽  
Vol 9 (1) ◽  
pp. 54 ◽  
Author(s):  
Guoqiang Sun ◽  
Tong Chen ◽  
Zhinong Wei ◽  
Yonghui Sun ◽  
Haixiang Zang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Carbon Price ◽  
Spiking Neural Networks ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Model Based ◽  
Mode Decomposition

scholarly journals Runoff forecasting model based on variational mode decomposition and artificial neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1633-1648
Author(s):  
Xin Jing ◽  
◽  
Jungang Luo ◽  
Shangyao Zhang ◽  
Na Wei
Keyword(s):  
Neural Networks ◽  
Water Resource Management ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Variational Mode Decomposition ◽  
Model Based ◽  
Runoff Forecasting ◽  
Mode Decomposition ◽  
Runoff Series ◽  
Correlation Information

<abstract> <p>Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecasting. However, most of the models simulate each decomposition sub-signals separately without considering the potential correlation information. A neoteric hybrid runoff forecasting model based on variational mode decomposition (VMD), convolution neural networks (CNN), and long short-term memory (LSTM) called VMD-CNN-LSTM, is proposed to improve the runoff forecasting performance further. The two-dimensional matrix containing both the time delay and correlation information among sub-signals decomposing by VMD is firstly applied to the CNN. The feature of the input matrix is then extracted by CNN and delivered to LSTM with more potential information. The experiment performed on monthly runoff data investigated from Huaxian and Xianyang hydrological stations at Wei River, China, demonstrates the VMD-superiority of CNN-LSTM to the baseline models, and robustness and stability of the forecasting of the VMD-CNN-LSTM for different leading times.</p> </abstract>

scholarly journals A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 177 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Monthly Precipitation ◽  
Variational Mode Decomposition ◽  
Precipitation Time Series ◽  
Mode Decomposition ◽  
Learning Machine

The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

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