A transferable turbidity estimation method for estimating clear-sky solar irradiance. (April 2023)

• Record Type: Journal Article
• Authors: Chen, Shanlin; Liang, Zhaojian; Dong, Peixin; Guo, Su; Li, Mengying
• Is Part Of: Renewable energy. Volume 206(2023)
• Page Start: 635
• Page End: 644
• Publication Date: 2023-04
• Subjects: Solar resourcing and forecasting -- Turbidity estimation -- Transferable model -- Clear-sky irradiance; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2023.02.096
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A transferable turbidity estimation method is proposed for estimating the turbidity and clear-sky solar irradiance. Instead of using on-site irradiance measurements (i.e., the local model), a transferable model is developed involving stations with sufficient information, and then applied at locations with limited data availability. Compared with the local method, the transferable model yields results with slightly higher discrepancies regrading normalized root mean squared error (nRMSE, 2.80% vs 2.75%). When compared with the Ineichen–Perez (PVLIB) model, the nRMSE of clear-sky global horizontal irradiance (GHIcs) estimation is reduced from 4.99% to 2.44%, and the normalized mean bias error (nMBE) is improved from -3.37% to 0.57%. The GHIcs estimation is comparable with physical models (i.e., McClear and REST2), where the McClear produces a nRMSE of 3.32% and the nMBE is 2.10%, while the REST2 generates results with an nRMSE of 2.55% and an nMBE of 1.30%. We further compare aforementioned models for day-ahead GHIcs forecasts using a day persistent way. GHIcs forecast from the transferable method has slightly lower discrepancies of nRMSE and nMBE than the physical models. Considering the complexity of physical models, the transferable turbidity estimation method with comparable performance demonstrates valuable potential for solar resourcing and forecasting applications. Highlights: The turbidity estimation method works for locations without sufficient data. The turbidity estimation model only requires common meteorological inputs. The proposed method shows a high performance in clear-sky irradiance estimation. The result of clear-sky irradiance estimation is comparable with physical models.