概率预测的颗粒式能源技术在次国家级的扩散
Nik Zielonka1, Xin Wen1, Evelina Trutnevyte1
1Renewable Energy Systems, Institute for Environmental Sciences (ISE), Section of Earth and Environmental Sciences, University of Geneva, Geneva CH-1211, Switzerland.
PNAS nexus
|October 18, 2023
概括
可能性S曲线模型改善了能源技术扩散预测. 瑞士分析显示,中位数预测更准确,伯塔兰菲和理查德斯的模型在太阳能,热和电动汽车方面表现优于其他模型.
科学领域:
- 能源技术的传播和传播.
- 减缓气候变化减缓气候变化减缓
- 下国家级政策分析.
背景情况:
- 能源技术扩散预测对于气候政策至关重要,但往往缺乏空间分辨率和不确定性量化.
- S曲线模型是用于设计设施的常见模型,但在结果中显示出显著的变化.
研究的目的:
- 开发一个概率方法,以在地方层面进行颗粒式能源技术扩散预测.
- 评估各种投影模型的准确性和不确定性,以告知模型选择.
主要方法:
- 利用历史时间序列数据进行技术扩散的概率预测.
- 在瑞士 (2000-2021) 的案例研究,检查了市级的太阳能光伏,热和电池电动汽车.
- 逻辑学,戈珀茨,巴斯,伯塔兰菲和一般化的理查德斯S曲线模型的准确性和不确定性的比较.
主要成果:
- 在所有测试的S曲线模型和技术中,概率预测的中位数始终优于确定性预测.
- 与物流,戈默茨和巴斯模型相比,伯塔兰菲和一般化的理查德斯模型显示出更高的准确性和清晰度.
- 模型性能因技术,市政和年份而异,表明了权衡和需要组合模型.
结论:
- 可能性的S曲线建模为颗粒式能源技术扩散预测提供了更高的准确性.
- 为了更可靠的预测,建议使用Bertalanffy和Richards等特定模型.
- 瑞士目前的扩散率表明,如果不加快采用,2050年的净零排放目标可能会被错过.
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