  In pursuit of a novel forecasting strategy for the energy market, we propose
a ResNet-inspired model which estimates solar and wind energy production using
weather images. The model is designed to capture high-frequency details while
producing realistically smooth energy production profiles. To this end, we show
the value of including multiple weather images from times preceding the
estimation time, and demonstrate that the model outperforms traditional deep
learning techniques and alternative state-of-the-art computer vision methods.
Training and testing are performed on a novel data set that focuses on the
state of California and spans the year 2019. The dataset, which is sourced from
NOAA and CAISO, is a secondary contribution of this work. Finally, multiple
topics in line with the motivation are proposed for future work.
