Ensemble model output statistics for the separation of direct and diffuse components from 1-min global irradiance

Abstract:

Separation models split diffuse and direct components of solar radiation from the global horizontal radiation. At the moment, all separation models only issue pbkp_redictions that are deterministic (as opposed to probabilistic). Since the best pbkp_rediction is necessarily probabilistic, a parametric post-processing framework called the ensemble model output statistics (EMOS) is introduced in this paper, to make probabilistic pbkp_redictions. EMOS takes the diffuse fractions pbkp_redicted by an ensemble of existing 1-min separation models, and outputs a pbkp_redictive distribution, with parameters optimized by maximum likelihood estimation. Clearly, the EMOS-based separation modeling goes beyond the current literature, in terms of uncertainty quantification. Eight popular separation models from the literature, with different architectures, are used to demonstrate the pbkp_redictive power of EMOS. Using 1-min high-quality radiometric data from seven stations in the USA and four stations in Europe, it is found that YANG2 is the best stand-alone model with an average RMSE of 21.8%, in terms of direct normal irradiance pbkp_rediction, contrasting the 26.3% of the previously reported best model, namely, ENGERER2. On the other hand, the EMOS post-processed pbkp_redictions have an average RMSE of 20.8%, which is lower than that of the best stand-alone model. Moreover, EMOS is shown superior to simple model averaging, in terms of continuous ranked probability score and ignorance score.

Año de publicación:

2020

Keywords:

• Direct horizontal irradiance
• Separation modeling
• Decomposition modeling
• Ensemble model output statistics
• Probabilistic modeling
• SOLAR RADIATION

Fuente:

scopus