Application of a novel grey Bernoulli model to predict the global consumption of renewable energy

Accurate prediction of renewable energy can provide an important basis for national energy security and the government to formulate policies. Therefore, according to the non-linearity of energy prediction system, this paper proposes a new nonlinear grey Bernoulli optimization model by the grey Berno...

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Published inEnergy reports Vol. 7; pp. 7200 - 7211
Main Authors Duan, Huiming, Wang, Siqi, He, Chenglin, Huang, Jiangbo
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2021
Elsevier
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Abstract Accurate prediction of renewable energy can provide an important basis for national energy security and the government to formulate policies. Therefore, according to the non-linearity of energy prediction system, this paper proposes a new nonlinear grey Bernoulli optimization model by the grey Bernoulli extended model, which is a nonlinear grey prediction model, and studies the properties of the new model. The order and nonlinear coefficient of the new model are optimized by Particle Swarm Optimization. Then, through the consumption of global renewable energy, such as solar, wind and hydropower as empirical analyses, the results of the four evaluation indicators show that the new model works better than the original model, which has higher prediction accuracy than before and makes the prediction model more applicable. At the same time, the model results were compared with the weighted grey model, Verhulst and the discrete grey model, and the new model has the highest accuracy. Finally, the new model is used to forecast the global consumption of wind, solar and hydropower energy in 2019–2023. The results will provide important forecasting information for global energy conservation and emission reduction policies.
AbstractList Accurate prediction of renewable energy can provide an important basis for national energy security and the government to formulate policies. Therefore, according to the non-linearity of energy prediction system, this paper proposes a new nonlinear grey Bernoulli optimization model by the grey Bernoulli extended model, which is a nonlinear grey prediction model, and studies the properties of the new model. The order and nonlinear coefficient of the new model are optimized by Particle Swarm Optimization. Then, through the consumption of global renewable energy, such as solar, wind and hydropower as empirical analyses, the results of the four evaluation indicators show that the new model works better than the original model, which has higher prediction accuracy than before and makes the prediction model more applicable. At the same time, the model results were compared with the weighted grey model, Verhulst and the discrete grey model, and the new model has the highest accuracy. Finally, the new model is used to forecast the global consumption of wind, solar and hydropower energy in 2019–2023. The results will provide important forecasting information for global energy conservation and emission reduction policies.
Author Duan, Huiming
Wang, Siqi
He, Chenglin
Huang, Jiangbo
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Keywords Bernoulli model
Grey prediction model
Optimization algorithm
Renewable energy consumption
Forecasting
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Snippet Accurate prediction of renewable energy can provide an important basis for national energy security and the government to formulate policies. Therefore,...
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SubjectTerms Bernoulli model
Forecasting
Grey prediction model
Optimization algorithm
Renewable energy consumption
Title Application of a novel grey Bernoulli model to predict the global consumption of renewable energy
URI https://dx.doi.org/10.1016/j.egyr.2021.10.070
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