综合性电力需求预测利用增强的挤压网和灵活的Coyote优化算法 RCPs SSPs和社会经济因素的组合
Bin Yang1, Bin Song2, Yadegar Abdi3,4
1School of Politics and Public Affairs Management, Sun Yat sen University, guangzhou, 510006, Guangdong, China.
Scientific reports
|July 4, 2025
概括
准确的电力需求预测对于在气候变化中管理资源至关重要. 这项研究引入了一种新的模型,将社会经济和气候数据与先进的算法相结合,以提高预测准确性和效率.
科学领域:
- 环境科学 环境科学
- 能源经济学 能源经济学
- 计算机科学 计算机科学
背景情况:
- 全球气温上升和极端热浪改变了能源消耗模式.
- 准确的电力消耗预测对于电力供应服务和资源管理至关重要.
研究的目的:
- 开发一个先进的电力需求预测模型.
- 提高电力消耗预测的准确性和效率.
主要方法:
- 综合社会经济路径 (SSP) 和代表性度路径 (RCP) 数据与气候因素.
- 使用紧的网络架构,增强了灵活的Coyote优化算法 (FCOA) 进行参数优化和融合加速.
- 用户Squeeze Net用于复杂的数据模式学习.
主要成果:
- 与传统方法相比,开发的预测模型显示出更高的准确性和效率.
- 整合FCOA显著优化了紧型网络的性能.
结论:
- 这种新的预测模型提供了一个可靠的工具,用于在不断变化的气候条件下预测电力需求.
- 这种方法为加强电力供应服务和资源管理提供了科学支持.
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