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This paper compares different forecasting methods and models to predictaverage values of solar irradiance with a sampling time of 15 min over aprediction horizon of up to 3 h. The methods considered only require historicsolar irradiance values, the current time and geographical location, i.e., noexogenous inputs are used. Nearest neighbor regression (NNR) and autoregressiveintegrated moving average (ARIMA) models are tested using differenthyperparameters, e.g., the number of lags, or the size of the training dataset, and data from different locations and seasons. The hyperparameters andtheir effect on the forecast quality are analyzed to identify properties whichare likely to lead to good forecasts. Using these properties, a reduced searchspace is derived to identify good forecasting models much faster.

Very Short Term Time-Series Forecasting of Solar Irradiance Without Exogenous Inputs