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

Abstract

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.