AI 中文总结
提出分布式JEPA自监督框架，通过掩蔽潜在表示预测与正则化，在异构能源时间序列上实现稳定表示，优于Transformer基线并增强鲁棒性。
AI 中文摘要
传统的能源预测解决方案依赖于特定任务的监督和能源资产表示，限制了其可迁移性以及跨异构资产捕捉通用时间动态的能力。为解决这一问题，我们提出了一种分布式联合嵌入预测架构（JEPA），用于从异构能源时间序列中进行自监督学习。该框架在共享嵌入空间内整合时间观测和上下文信息的同时，预测掩蔽时间段的潜在表示。为防止表示坍缩，训练结合了潜在空间预测目标与协方差和时间方差正则化。评估在数据退化场景下使用能源消耗和生成数据集进行，并与Transformer预测基线进行了比较。学习到的表示保持稳定（余弦相似度约0.98；有效秩185-235）。JEPA在建筑能源数据上达到了与Transformer相当的性能，在3/5个消费者集群中获得了更高的R²，并在9/10个未见光伏（PV）上优于基线（R²=0.73-0.88对比<0.45），同时表现出对缺失数据更强的鲁棒性。
英文摘要
Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability to capture general temporal dynamics across heterogeneous assets. We address this by proposing a distributed Joint Embedding Predictive Architecture (JEPA) for self-supervised learning from heterogeneous energy time-series. The framework predicts latent representations of masked temporal segments while integrating temporal observations and contextual information within a shared embedding space. To prevent representation collapse, training combines a latent-space predictive objective with covariance and temporal variance regularization. The evaluation was conducted on energy consumption and generation datasets under data-degradation scenarios and compared with a Transformer forecasting baseline. The learned representations remained stable (cosine similarity $\approx 0.98$; effective rank 185-235). JEPA achieved performance comparable to a Transformer on building energy data, higher $R^2$ in 3/5 consumer clusters, and outperformed the baseline on 9/10 unseen PVs ($R^2$=0.73-0.88 vs. <0.45), while showing greater robustness to missing data.
