论文标题
预测燃料燃烧相关的CO $ _2 $排放，由新颖的连续分数非线性灰色Bernoulli模型与灰狼优化器
Forecasting fuel combustion-related CO$_2$ emissions by a novel continuous fractional nonlinear grey Bernoulli model with Grey Wolf Optimizer
论文作者
论文摘要
对CO $ _2 $燃料燃烧的排放量对于决策者来说，确定有效减少计划的目标目标是至关重要的，进一步改善了能源政策和计划。为了准确预测燃料燃烧中中国CO $ _2 $排放的未来开发，本文开发了一种新型的连续分数非线性灰色Bernoulli模型。已知已经已经知道的分数非线性灰色Bernoulli模型，具有固定的一阶导数，在某种程度上会损害预测性能。为了解决这个问题，在新提出的模型中，将一个灵活变量引入衍生物的顺序中，从而使其摆脱了整数积累。为了进一步提高新提出的模型的性能，确定了新兴系数的元升级算法，即灰狼优化器（GWO）。为了证明有效性，通过与其他基准模型进行比较，将两个真实的例子和中国与燃料燃烧相关的CO $ _2 $排放用于模型验证，结果表明，拟议的模型表现优于竞争对手。因此，预计到2023年，与燃料燃烧相关的CO $ _2 $排放的未来开发趋势被预测，占1亿吨（MT）。根据预测，提供了一些建议，以遏制二氧化碳的排放。
Foresight of CO$_2$ emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and further to improve energy policies and plans. For the purpose of accurately forecasting the future development of China's CO$_2$ emissions from fuel combustion, a novel continuous fractional nonlinear grey Bernoulli model is developed in this paper. The fractional nonlinear grey Bernoulli model already in place is known that has a fixed first-order derivative that impairs the predictive performance to some extent. To address this problem, in the newly proposed model, a flexible variable is introduced into the order of derivative, freeing it from integer-order accumulation. In order to further improve the performance of the newly proposed model, a meta-heuristic algorithm, namely Grey Wolf Optimizer (GWO), is determined to the emerging coefficients. To demonstrate the effectiveness, two real examples and China's fuel combustion-related CO$_2$ emissions are used for model validation by comparing with other benchmark models, the results show the proposed model outperforms competitors. Thus, the future development trend of fuel combustion-related CO$_2$ emissions by 2023 are predicted, accounting for 10039.80 Million tons (Mt). In accordance with the forecasts, several suggestions are provided to curb carbon dioxide emissions.
