订单(交换)
可再生能源
应用数学
功率消耗
消费(社会学)
功率(物理)
计量经济学
中国
离散时间和连续时间
数学
数学优化
经济
计算机科学
环境科学
统计
工程类
物理
地理
热力学
电气工程
社会学
社会科学
考古
财务
作者
Lin Xia,Youyang Ren,Yuhong Wang,Yangyang Pan,Yanning Fu
标识
DOI:10.1016/j.renene.2024.121125
摘要
Accurately predicting renewable energy consumption is crucial for sustainable social and economic development, especially in China during its energy transition. This research introduces a novel dynamic fractional-order discrete grey multi-power model (DFDGMM(1,1,N)) to enable accurate forecasting of renewable energy consumption in China. The proposed method introduces a fractional-order accumulation operator and three power exponents that not only ensure the priority of new information, but also accurately capture the nonlinear traits of system data. It also incorporates a dynamic time delay function to account for the time lag between energy and economic development, enhancing the model's flexibility. Additionally, the study combines the whale optimization algorithm and the double-error idea to optimal parameter search. The proposed model is versatile and can be simplified into 14 other grey models. The case study demonstrates the model's impressive predictive accuracy, with a fitting error of 4.02% and a test error of 0.89%. The model is then employed to forecast renewable energy consumption in China, predicting a rapid annual growth rate of 17.25% from 2022 to 2030. Overall, this article successfully constructs a dynamic prediction model in theory and scientifically provides valuable data support for the nation's energy development planning in practice.
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