  Predicting the short-term power output of a photovoltaic panel is an
important task for the efficient management of smart grids. Short-term
forecasting at the minute scale, also known as nowcasting, can benefit from sky
images captured by regular cameras and installed close to the solar panel.
However, estimating the weather conditions from these images---sun intensity,
cloud appearance and movement, etc.---is a very challenging task that the
community has yet to solve with traditional computer vision techniques. In this
work, we propose to learn the relationship between sky appearance and the
future photovoltaic power output using deep learning. We train several variants
of convolutional neural networks which take historical photovoltaic power
values and sky images as input and estimate photovoltaic power in a very short
term future. In particular, we compare three different architectures based on:
a multi-layer perceptron (MLP), a convolutional neural network (CNN), and a
long short term memory (LSTM) module. We evaluate our approach quantitatively
on a dataset of photovoltaic power values and corresponding images gathered in
Kyoto, Japan. Our experiments reveal that the MLP network, already used
similarly in previous work, achieves an RMSE skill score of 7% over the
commonly-used persistence baseline on the 1-minute future photovoltaic power
prediction task. Our CNN-based network improves upon this with a 12% skill
score. In contrast, our LSTM-based model, which can learn the temporal
dependencies in the data, achieves a 21% RMSE skill score, thus outperforming
all other approaches.
