一种用于能源预测的自监督框架 JEPA
Distributed JEPA: A Self-Supervised Framework for Energy Forecasting
朋友，这篇论文介绍了一种叫 JEPA 的框架，专门用来预测能源消耗和发电量，效果比传统方法好，对缺失数据也更有韧性。
这篇论文提出了一种名为 JEPA 的自监督框架，用于处理异构能源时间序列数据。该框架通过预测被掩码的时间片段的潜在表示来学习共享嵌入空间，并使用协方差和时序方差正则化来防止表示坍塌。在建筑能源数据集上，JEPA 的表现与 Transformer 基准相当，在 3/5 的消费者集群中 R² 更高，并在 9/10 的未见光伏设备上表现更好（R²=0.73-0.88 vs. <0.45），同时表现出对缺失数据的更强鲁棒性。
Distributed JEPA: A Self-Supervised Framework for Energy Forecasting
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.