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.

翻译：本文首次尝试量化数据中心脱碳中碳强度预测的不确定性。我们识别并分析了两种类型的不确定性——时间不确定性与空间不确定性——并讨论了其系统影响。为应对量化碳强度预测不确定性中的时间动态问题，我们引入了一个基于保形预测的框架。评估结果表明，我们的技术在不同显著性水平下均能稳健地实现不确定性量化的目标覆盖范围。我们利用实际电力消耗轨迹进行了两项案例研究，分别聚焦于时间负载转移和空间负载转移。结果显示，将不确定性纳入调度决策可分别避免碳排放增加5%和14%。这些百分比对应一个20兆瓦数据中心集群中2.1吨和10.4吨的绝对碳排放减少量。