Accurate intraday forecasts are essential for power system operations, complementing day-ahead forecasts that gradually lose relevance as new information becomes available. This paper introduces a Bayesian updating mechanism that converts fully probabilistic day-ahead forecasts into intraday forecasts without retraining or re-inference. The approach conditions the Gaussian mixture output of a conditional variational autoencoder-based forecaster on observed measurements, yielding an updated distribution for the remaining horizon that preserves its probabilistic structure. This enables consistent point, quantile, and ensemble forecasts while remaining computationally efficient and suitable for real-time applications. Experiments on household electricity consumption and photovoltaic generation datasets demonstrate that the proposed method improves forecast accuracy up to 25% across likelihood-, sample-, quantile-, and point-based metrics. The largest gains occur in time steps with strong temporal correlation to observed data, and the use of pattern dictionary-based covariance structures further enhances performance. The results highlight a theoretically grounded framework for intraday forecasting in modern power systems.

翻译：准确的日内预测对于电力系统运行至关重要，它能够补充随着新信息出现而逐渐失去相关性的日前预测。本文提出了一种贝叶斯更新机制，可将完全概率化的日前预测转换为日内预测，而无需重新训练或重新推断。该方法以观测数据为条件，对基于条件变分自编码器的预测器输出的高斯混合分布进行调节，从而生成保留其概率结构的剩余时间范围更新分布。这使得点预测、分位数预测和集合预测能够保持一致性，同时保持计算效率并适用于实时应用。在家庭用电量和光伏发电数据集上的实验表明，所提方法在似然、样本、分位数和点预测等指标上可将预测精度提升高达25%。最大改进出现在与观测数据具有强时间相关性的时间步长中，而基于模式字典的协方差结构进一步提升了性能。研究结果为现代电力系统中的日内预测提供了一个理论基础坚实的框架。