COVID-19对风能和太阳能部门的影响以及基于机器学习的能源成本预测
Saheb Ghanbari Motlagh1,2, Fatemeh Razi Astaraei1, Mohammad Montazeri1
1Department of Renewable Energy Technologies and Energy Resources Engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
在全球范围内,COVID-19大流行影响了可再生能源的成本. 虽然一些国家由于通货膨胀和利率下降而减少了能源成本,但其他国家则因燃料价格和经济不稳定而面临增加.
科学领域:
- 能源经济学 能源经济学
- 环境政策 环境政策
- 技术经济分析 技术经济分析
背景情况:
- COVID-19大流行给全球能源市场带来了前所未有的挑战.
- 可再生能源行业经历了各种各样的影响,受经济和政策转变的影响.
研究的目的:
- 分析COVID-19大流行对七个国家的可再生能源的技术经济影响.
- 使用机器学习 (ML) 预测潜在的未来影响.
主要方法:
- 可再生能源系统的技术经济分析.
- 机器学习 (ML) 模型用于预测能源成本 (COE).
主要成果:
- 根据ML的预测,中国和印度可能会因大流行影响而增加COE.
- 由于成本降低和政策变化,尽管资本开支上升,但美国的采用增加了.
- 加拿大转向独立系统,减少COE,除了特定的电网/风力轮机场景.
- 德国和印度通过扩大可再生能源容量和成本管理降低了COE.
- 伊朗面临着影响COE的经济挑战,尽管电网连接的COE下降.
结论:
- 流行病对可再生能源成本的影响是国家特定的,受燃料价格,通货膨胀和政策的影响.
- 机器学习表明中国和印度未来可能会增加COE.
- 通过独立系统和政策支持的适应性可以减轻负面的经济影响.
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