  We present a novel approach to perform ground-based estimation and prediction
of the surface solar irradiance with the view to predicting photovoltaic energy
production. We propose the use of mini-batch k-means clustering to extract
features, referred to as per cluster number of pixels (PCNP), from sky images
taken by a low-cost fish eye camera. These features are first used to classify
the sky as clear or cloudy using a single hidden layer neural network; the
classification accuracy achieves 99.7%. If the sky is classified as cloudy, we
propose to use a deep neural network having as input features the PCNP to
predict intra-hour variability of the solar irradiance. Toward this objective,
in this paper, we focus on estimating the deep neural network model relating
the PCNP features and the solar irradiance, which is an important step before
performing the prediction task. The proposed deep learning-based estimation
approach is shown to have an accuracy of 95%.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning