Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption

Wu, Wenqing; Ma, Xin; Zeng, Bo; Wang, Yong; Cai, Wei

doi:10.1016/j.energy.2018.09.155

Cited by 103 publications

(54 citation statements)

References 51 publications

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“…It has been studied in eorem 1 [41] that r n is a monotonically decreasing function and a monotonically increasing function with respect to n if r ∈ (0, 1) and r ∈ (1, +∞), respectively. In particular, if r � 1, then r n ≡ 1.…”

Section: 1mentioning

confidence: 99%

“…From eorem 2 [41], we can see that A r D r is an identity matrix. us, for i > j we have (A rλ D rλ ) ij � 0, which means that the matrix D rλ defined as in (9) is the inverse matrix of A rλ .…”

Section: Complexitymentioning

confidence: 99%

“…Numerical results show that the fractional grey prediction models have much higher precision than the traditional GM(1,1) model. Modeling mechanism of the fractional grey models has been studied, see [17,29,35,[39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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Section: 1mentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

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A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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“…Wu et al. [51–53, 55, 58] investigated the novel fractional grey model FAGMO(1,1,k) to predict oil consumption. Ma et al.…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of oil‐water two‐phase flow for a multiple‐fractured horizontal well with multiple finite‐conductivity fractures in triple media carbonate reservoir

et al. 2020

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Carbonate reservoir is one of the important reservoir in the world. Because of the characteristics of carbonate reservoir, horizontal well and acid fracturing have become a key technology for efficiently developing carbonate reservoir. Establishing corresponding mathematical models and analyzing transient pressure behaviors of this type of well‐reservoir configuration can provide a better understanding of fluid flow patterns in formation as well as estimations of important parameters. A mathematical model for a oil‐water two‐phase flow multiple‐fractured horizontal well with multiple finite‐conductivity fractures in triple media carbonate reservoir by conceptualizing vugs as spherical shapes is presented in this article. A semi‐analytical solution is obtained in the Laplace domain by using source function theory, Laplace transformation, and superposition principle. Analysis of transient pressure responses indicates that nine characteristic flow periods of multiple‐fractured horizontal wells with multiple finite‐conductivity fractures in triple media carbonate reservoir can be identified. Parametric analysis shows that water saturation of matrix, vug and fracture system and acid fracture, fracture half‐length, fracture number, fracture spacing and acid fracture conductivity can significantly influence the transient pressure responses of multiple‐fractured horizontal wells with multiple finite‐conductivity fractures in triple media carbonate reservoir. The model presented in this article can be applied to obtain important parameters pertinent to reservoir or fracture by type curve matching, and it can also provide useful information for optimizing fracture parameters.

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“…erefore, it is signi cantly crucial to predict the energy consumption accurately for energy programming and supply plan of governments or energy companies. Numerous studies have been conducted to predict total energy consumption or other various energy consumptions, e.g., natural gas consumption [2], oil consumption [3], electricity consumption [4], nuclear energy consumption [5], wind energy and renewable energy consumption prediction [6], and so on. For obtaining better prediction result, lots of conventional statistical models and machine learning models were adopted to predict energy consumption, such as ridge regression [7], autoregressive integrated moving average model (ARIMA) [8], support vector regression (SVR) [9], and arti cial neural network (ANN) [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Zhang

She

2019

Complexity

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Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

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Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption

Cited by 103 publications

References 51 publications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

Dynamic analysis of oil‐water two‐phase flow for a multiple‐fractured horizontal well with multiple finite‐conductivity fractures in triple media carbonate reservoir

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

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