Despite the advances in the field of solar energy, improvements of solar
forecasting techniques, addressing the intermittent electricity production,
remain essential for securing its future integration into a wider energy
supply. A promising approach to anticipate irradiance changes consists of
modeling the cloud cover dynamics from ground taken or satellite images. This
work presents preliminary results on the application of deep Convolutional
Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky
images and exogenous variables. We evaluate the models on a set of irradiance
measurements and corresponding sky images collected in Palaiseau (France) over
8 months with a temporal resolution of 2 min. To outline the learning of neural
networks in the context of short-term irradiance forecasting, we implemented
visualisation techniques revealing the types of patterns recognised by trained
algorithms in sky images. In addition, we show that training models with past
samples of the same day improves their forecast skill, relative to the smart
persistence model based on the Mean Square Error, by around 10% on a 10 min
ahead prediction. These results emphasise the benefit of integrating previous
same-day data in short-term forecasting. This, in turn, can be achieved through
model fine tuning or using recurrent units to facilitate the extraction of
relevant temporal features from past data.

本文介绍了使用深度卷积神经网络预测太阳能辐射强度的初步结果，并探讨了训练算法的图像识别技术，和利用过去相同日期数据进行训练以提高短期预测技能的方法。