Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty. Separating these types of uncertainties can help decision-makers better understand where and to what extent the uncertainty is, thereby enhancing their confidence in the following decision-making. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty. Our method not only ensures the accuracy of load forecasting but also demonstrates the ability to separate the two types of uncertainties and be applicable to different levels of loads. The relevant code can be found at \url{https://anonymous.4open.science/r/DiffLoad-4714/}.

翻译：摘要：电力负荷预测在电力系统的决策制定中（包括机组组合与经济调度）发挥着关键作用。可再生能源的整合以及诸如新冠肺炎疫情等外部事件的发生，迅速增加了负荷预测中的不确定性。负荷预测的不确定性可分为两类：认知不确定性与偶然不确定性。分离这些不确定性类型有助于决策者更好地理解不确定性存在的位置及程度，从而增强其对后续决策的信心。本文提出了一种基于扩散的序列到序列结构来估计认知不确定性，并采用鲁棒加性柯西分布来估计偶然不确定性。我们的方法不仅确保了负荷预测的准确性，还展现出分离两种不确定性的能力，并适用于不同级别的负荷。相关代码可在 \url{https://anonymous.4open.science/r/DiffLoad-4714/} 获取。