Learning Physically Meaningful Representations of Energy Systems with Variational Autoencoders

Given the growing number of volatile energy producers and consumers and the limitations of traditional static load prediction models, we have analyzed the ability of neural networks to predict the loads of an electrical transformer and to understand the physical relationships in the electric grid. To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility of learning physical relationships of the electric grid. By adapting the prediction model to learn a physical variable, namely phase shift, we show that a variational autoencoder can learn physical relations. Our results show a significant improvement in terms of the correlation of the latent and physical variables by integrating prior knowledge in the form of the corresponding power values as the training objective.