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 Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 147 ◽  
Author(s):  
Shenghua Xiong ◽  
Chunfeng Wang ◽  
Zhenming Fang ◽  
Dan Ma
Keyword(s):  
Optimization Algorithm ◽  
Carbon Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Relevance Vector Regression ◽  
Accuracy And Stability

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

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