Abstract--We present a Fourier-enhanced recurrent neural network (RNN) for downscaling electrical loads. The model combines (i) a recurrent backbone driven by low-resolution inputs, (ii) explicit Fourier seasonal embeddings fused in latent space, and (iii) a self-attention layer that captures dependencies among high-resolution components within each period. Energy policy and infrastructure investment decisions require an integrated system-wide perspective that captures the interdependencies of supply, conversion, and end-use sectors, as well as feedback from macroeconomic, technology-cost, and policy drivers. Many such energy modeling systems exist [1], of which the National Energy Modeling System (NEMS), developed by the U.S. Energy Information Administration (EIA) [2], is widely used by policymakers and stakeholders for this very reason. However, as noted in the study of energy plant pollution studies provided by NEMS [3], using temporally and spatially averaged data may significantly miss essential features and pricing signals.