Choice of clear-sky model in solar forecasting

Abstract

This paper is concerned with the choice of clear-sky model in solar forecasting. This issue is discussed from three perspectives: (1) accessibility, (2) forecast performance, and (3) statistical properties. Accessibility refers to the time and effort involved in obtaining clear-sky irradiance through a clear-sky model. Forecast performance is analyzed through a new concept called "mean square error (MSE) scaling," which allows one to decompose the MSE of reference irradiance forecasts into three terms, each carrying a notion of predictability. The decomposition, however, resides on the assumption that the clear-sky index time series is stationary. In this regard, the stationarity assumption is investigated using statistical hypotheses. It is found that even the best clear-sky models, such as the REST2 model, are not able to produce a stationary clear-sky index time series. Instead, the time series is only local stationary, which, in the present context, means that its statistical properties change slowly with the value of clear-sky irradiance. Contrary to the common belief that a better clear-sky model leads to better forecasts, no evidence suggests that the more intricate REST2 has an advantage over the simpler Ineichen-Perez model, in terms of forecast performance. In that, accessibility becomes the primary concern when opting a clear-sky model for forecasting purposes. At this point, the McClear model, available as a web service for worldwide locations at 1-, 15-, and 60-min resolutions, is highly recommended.