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Despite the advances in the field of solar energy, improvements of solarforecasting techniques, addressing the intermittent electricity production,remain essential for securing its future integration into a wider energysupply. A promising approach to anticipate irradiance changes consists ofmodeling the cloud cover dynamics from ground taken or satellite images. Thiswork presents preliminary results on the application of deep ConvolutionalNeural Networks for 2 to 20 min irradiance forecasting using hemispherical skyimages and exogenous variables. We evaluate the models on a set of irradiancemeasurements and corresponding sky images collected in Palaiseau (France) over8 months with a temporal resolution of 2 min. To outline the learning of neuralnetworks in the context of short-term irradiance forecasting, we implementedvisualisation techniques revealing the types of patterns recognised by trainedalgorithms in sky images. In addition, we show that training models with pastsamples of the same day improves their forecast skill, relative to the smartpersistence model based on the Mean Square Error, by around 10% on a 10 minahead prediction. These results emphasise the benefit of integrating previoussame-day data in short-term forecasting. This, in turn, can be achieved throughmodel fine tuning or using recurrent units to facilitate the extraction ofrelevant temporal features from past data.

Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting