Very Short Term Time-Series Forecasting of Solar Irradiance Without Exogenous Inputs
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This paper compares different forecast 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 required. Nearest neighbor regression (NNR) and autoregressive integrated moving average (ARIMA) models are tested using different hyperparameters (e.g., the number of autoregressive lags, or the size of the training data set) and data from different locations and seasons. Based on a high number of different models, NNR is identified to be the more promising approach. 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 which can be used to identify good forecast models much faster. In a case study, the use of this search space is demonstrated by finding forecast models for different climatic situations.
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