A transferable turbidity estimation method for estimating clear-sky solar irradiance

被引:3
|
作者
Chen, Shanlin [1 ]
Liang, Zhaojian [1 ]
Dong, Peixin [1 ]
Guo, Su [2 ,3 ]
Li, Mengying [1 ,4 ]
机构
[1] Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong, Peoples R China
[2] Hohai Univ, Coll Energy & Elect Engn, Nanjing, Peoples R China
[3] Nanjing Jurun Informat Technol Co Ltd, Nanjing, Peoples R China
[4] Hong Kong Polytech Univ, Res Inst Smart Energy, Hong Kong, Peoples R China
关键词
Solar resourcing and forecasting; Turbidity estimation; Transferable model; Clear-sky irradiance; RADIATION; MODEL; MCCLEAR; REST2;
D O I
10.1016/j.renene.2023.02.096
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:635 / 644
页数:10
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