Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm

Sun, Wei; Zhang, Chongchong

doi:10.1016/j.apenergy.2018.09.118

Cited by 142 publications

(51 citation statements)

References 25 publications

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“…⃗ refers to the distance between the grey wolf and the prey, and ⃗ and ⃗ are the convergence factor and the swing factor, respectively. The calculation formula is shown in Equations (6) and 7: α stands for the head wolf, which leads the grey wolf group, followed by β, which assists the head wolf in making decisions; δ are the ordinary wolves commanded by α and β, and the underlying wolf is ω is commanded by α, β, and δ. Under the leadership of α, the group of grey wolves captures the prey, and the wolves gradually approach and track the prey by scent and other information.…”

Section: Grey Wolf Optimizer Algorithmmentioning

confidence: 99%

“…where t represents the iterations, C are the convergence factor and the swing factor, respectively. The calculation formula is shown in Equations (6) and 7:…”

Section: Grey Wolf Optimizer Algorithmmentioning

confidence: 99%

“…Afterwards, carbon emissions trading platforms were launched during 2013 and 2014. Furthermore, on December 19, 2017, the national emissions trading scheme (ETS) was officially implemented [5], which almost doubled the coverage of the carbon pricing system [6]. To date, the Chinese government has established eight pilot carbon markets that have valid developments for reducing greenhouse gas emissions [7].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Zhou

Huo

et al. 2019

Energies

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Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

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Section: Grey Wolf Optimizer Algorithmmentioning

confidence: 99%

“…where t represents the iterations, C are the convergence factor and the swing factor, respectively. The calculation formula is shown in Equations (6) and 7:…”

Section: Grey Wolf Optimizer Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Zhou

Huo

et al. 2019

Energies

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“…The main problem associated with WOA is moderate convergence speed so to get better performance chaotic WOA used in [40] and Hyper-heuristic approach used in [41]. Apart from engineering problem, WOA used in wrapper feature selection [42], in constrained engineering problem [43], in parameter extraction of solar PV models [44], in weak feature extraction from multi component signal [45], to sustain the balance between exploration and exploitation [46], in optimal reactive power dispatch [47], to enhance the power system stability [48], in cost minimization of Micro-Grid [49], in analysis and forecasting of the carbon price [50]. These wide applications motivated authors to develop a demand side management based strategy on the basis of WOA Pseudo code for WOA algorithm is illustrated in Algorithm 1.…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

A demand side management control strategy using Whale optimization algorithm

Sharma

2019

SN Appl. Sci.

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In recent years, demand side management programs are in the spotlight due to the evolution of the smart grid and consumer-centric policies. Demand side management program contains many objectives one of the prime objective is to manage energy demand by certain change in consumer demand. This can be achieved by various methods such as financial discount and change in behavior through imparting education to support the stressed conditions of the grid. This paper demonstrates demand side management strategies based upon strategic conservation, peak clipping and load shifting techniques for future smart grids. The grid contains large number of controllable devices. The day before strategic conservation, peak clipping and load shifting techniques discussed in this paper are mathematically derived for minimization problem. A heuristic-based Whale optimization algorithm (WOA) was developed for solving this problem of minimization. Simulations are conducted on a test smart grid that contains a variation in loads in two service areas, one with residential consumers, and another with commercial consumers. WOA proves its efficacy by comparing the results with spider monkey optimization and biogeography based optimization. The simulation results show that proposed demand side management strategies achieve substantial savings, while reducing the peak load demand of the smart grid.

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“…Typical applications of such strategy include fault diagnosis [38,39], biosignal analysis [40,41], time series forecasting [42,43], and so on [44][45][46]. Following this strategy, a "decomposition and ensemble" framework has become very popular in the field of energy forecasting, such as wind speed forecasting [47,48], load forecasting [49][50][51] and price forecasting [8,11,52,53] in recent years. Ren et al integrated empirical mode decomposition (EMD) and SVR to forecast wind speed.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Zhou

et al. 2019

Energies

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As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

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Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm

Cited by 142 publications

References 25 publications

Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

A demand side management control strategy using Whale optimization algorithm

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

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