  Accurate renewable energy production forecasting has become a priority as the
share of intermittent energy sources on the grid increases. Recent work has
shown that convolutional deep learning models can successfully be applied to
forecast weather maps. Building on this capability, we propose a
ResNet-inspired model that estimates solar and wind power production based on
weather maps. By capturing both spatial and temporal correlations using
convolutional neural networks with stacked input frames, the model is designed
to capture the complex dynamics governing these energy sources. A dataset that
focuses on the state of California is constructed and made available as a
secondary contribution of the work. We demonstrate that our novel model
outperforms traditional deep learning techniques: it predicts an accurate power
production profile that is smooth and includes high-frequency details.
