  The need to forecast solar irradiation at a specific location over short-time
horizons has acquired immense importance. In this paper, we report on analyses
results involving statistical and machine learning techniques to predict hourly
horizontal solar irradiation at one-hour ahead horizon using data sets from
three different cities in the U.S. with different climatic conditions. A simple
forecast approach that assumes consecutive days are identical serves as a
baseline model against which to compare competing forecast alternatives. One
approach is to use seasonal ARIMA models. Surprisingly, such models are found
to be poorer than the simple forecast. To account for seasonal variability and
capture short-term fluctuations, cloud cover is an obvious variable to
consider. Monthly models with cloud cover as regressor were found to outperform
the simple forecast model. More sophisticated lagged moving average (LMX)
models were also evaluated, and one of the variants, LMX2, identified at
monthly time scales, proved to be the best choice. Finally, the LMX2 model is
compared against artificial neural network (ANN) models and the latter proved
to be more accurate. The companion paper will present algorithms and results of
how such models can be used for 4-hr rolling horizon and 24-hr ahead
forecasting.
