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。

DiffLoad：基于扩散模型的负荷预测不确定性量化

DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion  Model

We propose a new prior distribution for classical (nonhierarchical) logistic
regression models, constructed by first scaling all nonbinary variables to have
mean 0 and standard deviation 0.5, and then placing independent Student-$t$
prior distributions on the coefficients. As a default choice, we recommend the
Cauchy distribution with center 0 and scale 2.5, which in the simplest setting
is a longer-tailed version of the distribution attained by assuming one-half
additional success and one-half additional failure in a logistic regression.
Cross-validation on a corpus of datasets shows the Cauchy class of prior
distributions to outperform existing implementations of Gaussian and Laplace
priors. We recommend this prior distribution as a default choice for routine
applied use. It has the advantage of always giving answers, even when there is
complete separation in logistic regression (a common problem, even when the
sample size is large and the number of predictors is small), and also
automatically applying more shrinkage to higher-order interactions. This can be
useful in routine data analysis as well as in automated procedures such as
chained equations for missing-data imputation. We implement a procedure to fit
generalized linear models in R with the Student-$t$ prior distribution by
incorporating an approximate EM algorithm into the usual iteratively weighted
least squares. We illustrate with several applications, including a series of
logistic regressions predicting voting preferences, a small bioassay
experiment, and an imputation model for a public health data set.

通过在非层次逻辑回归模型系数上独立使用学生 - t 先前分布构造一个新的先前分布，具有更好的性能，并且自动应用更多的收缩到高阶交互项和自动生成更好的结果。