This paper represents the first effort to quantify uncertainty in carbon intensity forecasting for datacenter decarbonization. We identify and analyze two types of uncertainty -- temporal and spatial -- and discuss their system implications. To address the temporal dynamics in quantifying uncertainty for carbon intensity forecasting, we introduce a conformal prediction-based framework. Evaluation results show that our technique robustly achieves target coverages in uncertainty quantification across various significance levels. We conduct two case studies using production power traces, focusing on temporal and spatial load shifting respectively. The results show that incorporating uncertainty into scheduling decisions can prevent a 5% and 14% increase in carbon emissions, respectively. These percentages translate to an absolute reduction of 2.1 and 10.4 tons of carbon emissions in a 20 MW datacenter cluster.

本研究首次量化数据中心减碳的碳强度预测不确定性，并分析了时间和空间这两种不确定性类型及其对系统的影响。通过引入基于一致预测的框架，我们解决了碳强度预测中与时间动态相关的不确定性问题。评估结果表明，我们的技术在各种显著性水平上稳健地实现了目标覆盖率的不确定性量化。我们通过两个案例研究使用产生的能量数据，分别关注时间和空间的负荷转移。结果显示，将不确定性纳入调度决策可以分别防止碳排放量增加5%和14%，这转化为在一个20兆瓦的数据中心群组中，绝对减少了2.1和10.4吨的碳排放量。

数据中心的不确定性感知脱碳