Integration of intermittent renewable energy sources into electric grids in
large proportions is challenging. A well-established approach aimed at
addressing this difficulty involves the anticipation of the upcoming energy
supply variability to adapt the response of the grid. In solar energy,
short-term changes in electricity production caused by occluding clouds can be
predicted at different time scales from all-sky cameras (up to 30-min ahead)
and satellite observations (up to 6h ahead). In this study, we integrate these
two complementary points of view on the cloud cover in a single machine
learning framework to improve intra-hour (up to 60-min ahead) irradiance
forecasting. Both deterministic and probabilistic predictions are evaluated in
different weather conditions (clear-sky, cloudy, overcast) and with different
input configurations (sky images, satellite observations and/or past irradiance
values). Our results show that the hybrid model benefits predictions in
clear-sky conditions and improves longer-term forecasting. This study lays the
groundwork for future novel approaches of combining sky images and satellite
observations in a single learning framework to advance solar nowcasting.

通过整合来自全天候摄像头和卫星观测的云覆盖率补充信息，本研究利用机器学习将两种信息融合起来，提高了日照预报的准确性。结果表明，混合模型对于晴朗天气的预测效果更好，同时也提高了更长期的预测能力。

Omnivision 预测：将卫星观测和天空图像相结合，以提高小时内太阳能预测的准确性

Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions

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.

本文提出通过深度学习，学习天空外观与未来光伏发电功率之间的关系，并将历史光伏发电功率值和天空图像作为输入，利用多层感知器、卷积神经网络和长短期记忆模型三种不同的体系结构，建立短期预测模型，从而实现对 1 分钟后光伏发电功率的预测，结果表明，基于 LSTM 模型的预测精度最高。