A self-supervised framework, Distributed JEPA, is proposed for energy forecasting. It uses a distributed Joint Embedding Predictive Architecture to predict latent representations of masked temporal segments and integrates temporal observations and contextual information. JEPA is evaluated on energy consumption and generation datasets and shows comparable or better performance than a Transformer baseline.