Identifying transferability as the key issue in ML DFT, how can we make the training of energies more dataefficient and use this to embed transferability in ML DFT models? To address this problem, we introduce real-space energy learning and apply it to machine-learn a DFA for correlation energy (a crucial DFT quantity that has to be approximated). This approach expands each system's single energy data point into thousands of energy data points for the training. During the learning of our DFAs, each point in space contributes to the loss function, which we call local energy loss (LES). Crucially, LES penalizes error cancellations between energy contributions from different regions in space and thereby enhances transferability. With LES, every system in the training set effectively becomes an entire energy dataset.