  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/}.
