  The energy available in Micro Grid (MG) that is powered by solar energy is
tightly related to the weather conditions in the moment of generation. Very
short-term forecast of solar irradiance provides the MG with the capability of
automatically controlling the dispatch of energy. To achieve this, we propose a
method for statistical quantification of cloud features extracted from
long-ware infrared (IR) images to forecast the Clear Sky Index (CSI). The
images are obtained using a data acquisition system (DAQ) mounted on a solar
tracker. We explain how to remove cyclostationary bias in the data caused by
the devices in the own DAQ. We investigate a method to obtain the CSI, after
the detrending of Global Horizontal Irradiance (GHI) measurements. We propose a
method to fusion multiple exposures of circumsolar visible (VI) light images.
We implement a method for extracting physical features using radiometric
measurements of the IR camera. We introduce a model to remove from IR images
both the effect of the atmosphere scatter radiation, and the effect of the Sun
direct radiation. We explain how to model of diffuse radiation of the IR camera
window, which is produce by water spots and dust particles stack to the
germanium lens of the DAQ enclosure. The frames, that were used to model the
camera window, are selected using an atmospheric condition model. This model
classifies the sky four different categories: clear, cumulus, stratus, and
nimbus. We introduce a geometric transformation of the size of the pixels to
their actual dimension in a plane of the atmosphere which is at a given height.
This transformation is performed according to the elevation angle of the Sun
and field of view (FOV) of the camera. We compare the error between the
transformation and anapproximation of transformation.
