| 摘要(英) |
Surface solar radiation (or global horizontal irradiance, GHI) data is critical for photovoltaic and electric power companies to monitor power generation efficiency of photovoltaic system. However, GHI highly varied with sky conditions which mainly due to solar position and clouds variations. The typically temporal resolution of GHI product is 1-hour resolution, which might not be enough to represent the GHI variability. In this study, we used Himawari-8 (H8) satellite 10-min resolution data and applied a one-layer radiation transfer model to derived high temporal resolution GHI data. Our model includes the calculations for the scattering and absorption from aerosol, ozone, water vapor, gases and clouds. The observational GHI data has been used to construct relationships between solar spectrum and satellite bands, and further create an empirical function. Furthermore, a clear-sky identification method with sky index (i.e., clear sky, partly clear sky and cloudy sky) is proposed to evaluate the model derived-GHI performance in different sky conditions.
Data from the CWB Chiayi station was used to evaluate the performance of our model deriving results. Statistical results show that the rMBE, rRMSE, and r2 are -1.7% vs -5.9%, 6.8% vs 22.7% and 0.98 vs 0.84 for under clear sky and unclear sky, respectively. The performance of model under clear sky is outstanding, and the performance of model under unclear sky is moderately, and according to the analysis of the results under different sky conditions, we found that the largest error in the model is caused by the clouds, which could reduce the r2 value of derived GHI from 0.98 to 0.47.
We also found that when we consider sky-index-dependent empirical functions using in the model, the r2 value improved from 0.84 to 0.86. Compared to the CWB 1-hr resolution GHI product (rMBE: -1.5%, rRMSE: 11.2% and r2: 0.94), our high-resolution (10-min computation and average to 1-hr for comparison) show the rMBE, rRMSE and r2 value are -6.2%, 14.3% and 0.92, respectively. In order to evaluate the spatial and temporal applicability of our model, we found that the farther away from the station which used to establish the linear relationships, the error of the derived GHI would increase. With the basement of our model, if we could define the effective radius of weather stations in Taiwan, we can establish different linear regression equations at different places, so that GHI can be derived in more region. |
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