Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?

Ground-based radiometry is the most reliable method for obtaining data for solar energy applications. However, due to potential calibration and data quality-control issues, as well as the high cost associated with instrumentation, reliable and long-term ground-based measurements are scarce. For this reason, irradiance data from alternative sources, such as modeled or remote-sensed data, are of interest. Nonetheless, data coming from these sources are known to have systematic errors that may deteriorate the performance of subsequent applications. This study aims to evaluate the quality of solar forecasts generated using two types of irradiance data, namely, ground-based and bias-corrected satellite-derived global horizontal irradiance using error decomposition frameworks. Hourly forecasts are generated using 5 high-performance machine-learning models, with ground-based data from 15 research-grade monitoring stations in Europe, South America, and Africa, and satellite-derived data at the collocated pixels, separately. On top of these component forecasts, ensemble forecasts are also evaluated. Instead of limiting the forecast verification to a set of accuracy measures (e.g., mean bias error or root mean square error), the Murphy–Winkler forecast verification framework is considered, in which the joint, conditional, and marginal distributions of observations and forecasts are examined. The framework describes the qualities of forecast, such as calibration, conditional bias, or discrimination, with quantitative measures. Hence, it provides additional insights to the traditional measure-oriented approach. The main results show that the forecasts generated using bias-corrected satellite-derived data have comparable quality as compared to those generated using ground-based data.