Distributed JEPA: A Self-Supervised Framework for Energy Forecasting

2026-09-15

AI Summary

本文提出了一种分布式JEPA框架，通过自监督学习方法预测异构能源时间序列数据，解决了传统能源预测模型泛化能力不足的问题。

Abstract

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.

Problem

• energy forecasting
• self-supervised learning
• heterogeneous assets
• temporal dynamics
• representation collapse

Innovation

• Distributed JEPA
• self-supervised learning
• latent representation prediction
• covariance and temporal variance regularization
• robust to missing data