Electrical load forecasting is of great significance for the decision makings
in power systems, such as unit commitment and energy management. In recent
years, various self-supervised neural network-based methods have been applied
to electrical load forecasting to improve forecasting accuracy and capture
uncertainties. However, most current methods are based on Gaussian likelihood
methods, which aim to accurately estimate the distribution expectation under a
given covariate. This kind of approach is difficult to adapt to situations
where temporal data has a distribution shift and outliers. In this paper, we
propose a diffusion-based Seq2seq structure to estimate epistemic uncertainty
and use the robust additive Cauchy distribution to estimate aleatoric
uncertainty. Rather than accurately forecasting conditional expectations, we
demonstrate our method's ability in separating two types of uncertainties and
dealing with the mutant scenarios.

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，并使用强健性添加的柯西分布来估计位置不确定性的方法来预测电气负载预测。与精确预测条件期望相比，我们展示了我们方法在分离两种不确定性和解决突变情况方面的能力。