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

Energy ◽  
2018 ◽  
Vol 165 ◽  
pp. 223-234 ◽  
Author(s):  
Wenqing Wu ◽  
Xin Ma ◽  
Bo Zeng ◽  
Yong Wang ◽  
Wei Cai
Keyword(s):  
Energy Consumption ◽  
Nuclear Energy ◽  
Grey Model ◽  
The Novel

PREREQUISITES FOR THE DEVELOPMENT OF NUCLEAR ENERGY IN THE GLOBAL ECONOMY

2020 ◽  
Vol 2 (3) ◽  
pp. 153-158
Author(s):  
E. V. YANUSIK ◽  
Keyword(s):  
Energy Consumption ◽  
Economic Efficiency ◽  
Global Economy ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Energy ◽  
World Market ◽  
Global Energy ◽  
The World ◽  
Global Energy Consumption

The article discusses the main prerequisites for the development of nuclear energy in the global econo-my, also defines nuclear energy and discusses the structure of global energy consumption. The article proves that the crucial prerequisite for the development of nuclear energy in the world market is the economic efficiency of nuclear power plants.

The path to a 2025 nuclear-free Taiwan: An analysis of dynamic competition among emissions, energy, and economy

2020 ◽  
pp. 0958305X2094998
Author(s):  
Chun Chih Chen
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Nuclear Energy ◽  
Fossil Fuel ◽  
Percentage Error ◽  
Kuznets Curve ◽  
Energy Mix ◽  
Improve Energy Efficiency ◽  
Relationship Analysis ◽  
The Government

Taiwan intends to be nuclear free by 2025. This study employs the Lotka–Volterra competition model for sustainable development to analyze the emissions–energy–economy (3Es) issue to make appropriate policy suggestions for a nuclear-free transition. It also offers a new approach to naming the 3E relationship. The literature review shows that the environmental Kuznets curve accompanies the feedback and conservation hypotheses. In the 3E dynamics relationship analysis, the model shows a good mean absolute percentage error (<15%) for the model estimation. The key findings are as follows: 1) the fossil fuel-led economy exists; 2) CO2 emissions are reduced with nuclear energy consumption; 3) renewable energy is far from scale; 4) a complementary effect exists between fossil fuel and nuclear energy consumption; and 5) gas retrofitting and phasing out of nuclear seem imminent. In the energy transition, Taiwan drastically cuts nuclear energy without considering energy diversity due to which troubles might ensue. The priority issue for Taiwan’s energy mix is energy security. To deal with these concerns, this study suggests the government could improve energy efficiency, build a smart grid, develop carbon capture and storage, and reconsider putting nuclear energy back into the energy mix before renewable energy is scaled.

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

2012 ◽  
Vol 93 ◽  
pp. 432-443 ◽  
Author(s):  
Ling Tang ◽  
Lean Yu ◽  
Shuai Wang ◽  
Jianping Li ◽  
Shouyang Wang
Keyword(s):  
Energy Consumption ◽  
Ensemble Learning ◽  
Nuclear Energy ◽  
Learning Paradigm

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

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