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：电力负荷预测在电力系统决策（如机组组合与经济调度）中起着至关重要的作用。可再生能源的整合以及外部事件（如COVID-19疫情）的发生，迅速增加了负荷预测中的不确定性。负荷预测中的不确定性可分为两类：认知不确定性和偶然不确定性。区分这两种不确定性有助于决策者更好地理解不确定性的来源与程度，从而增强后续决策的信心。本文提出一种基于扩散模型的Seq2Seq结构来估计认知不确定性，并采用鲁棒的加性柯西分布来估计偶然不确定性。我们的方法不仅保证了负荷预测的准确性，还展现了区分两类不确定性以及适用于不同负荷水平的能力。相关代码可在 \url{https://anonymous.4open.science/r/DiffLoad-4714/} 获取。