多样性(控制论)
补语(音乐)
计算机科学
能量转换
能量(信号处理)
人工智能
机器学习
物理
医学
基因
量子力学
表型
生物化学
病理
化学
互补
替代医学
灵丹妙药
出处
期刊:Nature Energy
[Springer Nature]
日期:2021-01-22
卷期号:6 (2): 121-122
被引量:15
标识
DOI:10.1038/s41560-021-00779-9
摘要
Energy scenarios project future possibilities based on a variety of assumptions, yet do not fully account for inherent friction in the energy transition, particularly over the near term. A new study shows how machine learning can complement existing scenario tools by incorporating lessons from the past into projections for the future.
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