  We report a data-parsimonious machine learning model for short-term
forecasting of solar irradiance. The model inputs include sky camera images
that are reduced to scalar features to meet data transmission constraints. The
output irradiance values are transformed to focus on unknown short-term
dynamics. Inspired by control theory, a noise input is used to reflect
unmeasured variables and is shown to improve model predictions, often
considerably. Five years of data from the NREL Solar Radiation Research
Laboratory were used to create three rolling train-validate sets and determine
the best representations for time, the optimal span of input measurements, and
the most impactful model input data (features). For the chosen test data, the
model achieves a mean absolute error of 74.34 $W/m^2$ compared to a baseline
134.35 $W/m^2$ using the persistence of cloudiness model.
