Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model

The trends of solar radiation are not easy to capture and become especially hard to predict when weather conditions change dramatically, such as with clouds blocking the sun. At present, the better performing methods to forecast solar radiation are time series methods, artificial neural networks and stochastic models. This paper will describe a new and efficient method capable of forecasting 1-h ahead solar radiation during cloudy days. The method combines an autoregressive (AR) model with a dynamical system model. In addition, the difference of solar radiation values at present and lag one time step is used as a correction to a predicted value, improving the forecasting accuracy by 30% compared to models without this correction.

► We use Fourier series to deseason global solar radiation.
► We introduce two models for forecasting deseasoned global solar radiation which are AR(2) and Lucheroni model.
► We combine the AR(2) and Lucheroni models to enhance the forecast.
► We add a new component, which we call the fixed component, to further improve the forecasting ability.
► We compare the new model with other popular solar radiation forecasting models and show advantages of our model.