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

• Published in: Solar energy 2020-09, Vol.208, p.591-603
• Main Authors: Yang, Dazhi, Gueymard, Christian A.
• Format: Article
• Language: English
• Subjects: Decomposition modeling; Direct horizontal irradiance; Ensemble model output statistics; Irradiance; Maximum likelihood estimation; Parameter estimation; Post-production processing; Predictions; Probabilistic modeling; Separation; Separation modeling; Solar energy; Solar radiation; Stations; Statistical analysis; Statistics
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2020.05.082

•Ensemble modeling for 1-min diffuse fraction is discussed.•The raw ensemble is post-processed using ensemble model output statistics (EMOS).•EMOS outperforms the best stand-alone models such as Yang2 and Engerer2.•EMOS is superior to simple model blending in terms of CRPS and ignorance score. Separation models split diffuse and direct components of solar radiation from the global horizontal radiation. At the moment, all separation models only issue predictions that are deterministic (as opposed to probabilistic). Since the best prediction is necessarily probabilistic, a parametric post-processing framework called the ensemble model output statistics (EMOS) is introduced in this paper, to make probabilistic predictions. EMOS takes the diffuse fractions predicted by an ensemble of existing 1-min separation models, and outputs a predictive 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 predictive 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 prediction, contrasting the 26.3% of the previously reported best model, namely, Engerer2. On the other hand, the EMOS post-processed predictions 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.