环境政策驱动的电力消耗预测:一个新的缓冲校正的豪斯多夫分数灰色模型,由两阶段增强的多目标优化提供信息
Yuansheng Qian1, Zhijie Zhu2, Xinsong Niu3
1Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China.
Journal of environmental management
|February 25, 2025
概括
本研究引入了一种新的混合模型,用于长期预测电力消耗,这对于碳减排战略至关重要. 先进的模型提高了预测准确度,有助于可持续发展的城市发展和环境规划.
科学领域:
- 能源经济与能源政策
- 环境科学 环境科学
- 计算智能是一种计算智能.
背景情况:
- 电力消耗与碳排放密切相关,准确的预测对于实现碳中和目标至关重要.
- 长期电力消耗预测是电力系统规划和城市发展的基础,但比短期预测还少被探索.
- 现有的预测模型可能会在外部冲击和参数优化方面扎,需要先进的方法.
研究的目的:
- 开发和验证一种创新的混合豪斯多夫分数灰色模型 (HfGM),用于长期电力消耗预测.
- 通过整合一个弱化缓冲操作符 (WBo) 和优化其参数使用一个新的多目标粘液模具算法来增强HfGM.
- 应用经过验证的模型来预测中国在第15个五年计划期间的电力消耗,并评估环境影响.
主要方法:
- 开发一种混合的豪斯多夫分数灰色模型 (HfGM),集成一个弱化缓冲运算符 (WBo),以减轻数据干扰.
- 实施一个双阶段,多目标增强的粘液模具算法,以优化HfGM核心参数,以达到帕雷托优化.
- 使用电力消耗数据对拟议的HfGM与比较模型进行实证验证.
主要成果:
- 拟议的HfGM在预测电力消耗方面,与现有模型相比,表现优越.
- 该模型已成功应用于预测中国的长期电力消费趋势.
- 评估了与预测的电力消耗相关的环境影响.
结论:
- 开发的HfGM为长期电力消耗预测提供了强大而准确的方法.
- 该研究为将环境管理纳入电力需求规划提供了关键的见解,以支持碳减排目标.
- 提供政策建议,以协助当局管理需求增长和实现可持续发展目标.
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