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.

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

Carbon price forecasting with variational mode decomposition and optimal combined model

2019 ◽  
Vol 519 ◽  
pp. 140-158 ◽  
Author(s):  
Jiaming Zhu ◽  
Peng Wu ◽  
Huayou Chen ◽  
Jinpei Liu ◽  
Ligang Zhou
Keyword(s):  
Carbon Price ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Combined Model ◽  
Mode Decomposition

scholarly journals MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4622 ◽  
Author(s):  
Huichao Yan ◽  
Ting Xu ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Hongping Hu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Signal Denoising ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Whale Optimization

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

scholarly journals Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm

2019 ◽  
Vol 11 (2) ◽  
pp. 126 ◽  
Author(s):  
Hongxu Li ◽  
Jianhua Chang ◽  
Fan Xu ◽  
Zhenxing Liu ◽  
Zhenbo Yang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Whale Optimization Algorithm ◽  
Variational Mode Decomposition ◽  
Bhattacharyya Distance ◽  
Detection Range ◽  
Sensing Technology ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Satisfactory Analysis

Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty α was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km.

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

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