一个模型搞定电力系统多实体概率预测，省去逐个建模的麻烦。
One Model to Forecast Them All and in Entity Distributions Bind Them
- 用条件变分自编码器统一建模，支持单模型预测多实体
- 在家庭用电数据上超越传统方法，提升预测精度与不确定性捕捉能力
- 适合需要高效、可扩展概率预测的能源系统研究者
电力系统中的概率预测常涉及家庭、馈线、风力发电机等多实体数据，生成可靠的实体特定预测面临巨大挑战。传统方法需为每个实体单独训练模型，效率低且难以扩展。本文提出 GUIDE-VAE——一种条件变分自编码器，仅用一个模型即可实现多实体的概率预测。该模型输出灵活，可生成可解释的点估计或完整的概率分布，得益于其先进的协方差组合结构，能有效捕捉不确定性与时间依赖性。以家庭用电数据为例进行评估，该数据具有多实体和高度随机性特征。实验结果表明，GUIDE-VAE 在关键指标上优于传统的分位数回归方法，同时具备良好的可扩展性与通用性，是概率预测任务的强大工具，未来可推广至更广泛场景。
原文摘要 · Abstract (English)
Probabilistic forecasting in power systems often involves multi-entity datasets like households, feeders, and wind turbines, where generating reliable entity-specific forecasts presents significant challenges. Traditional approaches require training individual models for each entity, making them inefficient and hard to scale. This study addresses this problem using GUIDE-VAE, a conditional variational autoencoder that allows entity-specific probabilistic forecasting using a single model. GUIDE-VAE provides flexible outputs, ranging from interpretable point estimates to full probability distributions, thanks to its advanced covariance composition structure. These distributions capture uncertainty and temporal dependencies, offering richer insights than traditional methods. To evaluate our GUIDE-VAE-based forecaster, we use household electricity consumption data as a case study due to its multi-entity and highly stochastic nature. Experimental results demonstrate that GUIDE-VAE outperforms conventional quantile regression techniques across key metrics while ensuring scalability and versatility. These features make GUIDE-VAE a powerful and generalizable tool for probabilistic forecasting tasks, with potential applications beyond household electricity consumption.
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