  This paper compares different forecasting methods and models to predict
average values of solar irradiance with a sampling time of 15 min over a
prediction horizon of up to 3 h. The methods considered only require historic
solar irradiance values, the current time and geographical location, i.e., no
exogenous inputs are used. Nearest neighbor regression (NNR) and autoregressive
integrated moving average (ARIMA) models are tested using different
hyperparameters, e.g., the number of lags, or the size of the training data
set, and data from different locations and seasons. The hyperparameters and
their effect on the forecast quality are analyzed to identify properties which
are likely to lead to good forecasts. Using these properties, a reduced search
space is derived to identify good forecasting models much faster.
