| 摘 要： |
Accurate estimation of surface solar radiation (SSR) is crucial for photovoltaic (PV) systems design and solar PV power plants site selection. However, the SSR observations often suffer from inhomogeneity issues (e.g., aging equipment and instrument replacement) and low spatial-temporal coverage, which constrained the management and development of PV systems, particularly for countries with huge solar energy investments (e.g., China). To address them, we proposed seven different models (including four machine learning models, two empirical models and a multiple linear regression model) for accurate prediction of daily SSR using conventional meteorological data. Our results showed that the support vector machine (SVM) outperformed other models in estimating daily SSR. Based on SSR data corrected by the SVM model, the sharp downtrend in SSR (-9.64 W m(- 2) decade(-1)) due to the sensitivity drift of instrument aging before 1990 was moderated (-2.36 W m(-2) decade(-1)) in China; and the abnormal jump caused by instrument replacement in SSR observations during 1990-1993 was removed. Furthermore, by combining the advantages of large network of conventional meteorological data and SVM model, we extended the limited station-based SSR data (-100 meteorological stations) to include longer period and larger spatial coverage (2185 meteorological stations, 1971-2016) in China. By coupling the grid based method and PV power model, we created reliable SSR and PV power maps with higher temporal and spatial resolution over China. Spatially, the main distribution of high SSR and PV power were in the northwestern China, Tibetan Plateau and some coastal areas in the southern China. Temporally, the PV power experienced a significant drop during 1971-2016 (-1.94 kWh m(-2) decade(-1)) due to the significant decline in SSR (-0.91 W m(-2) decade(-1)). Above all, the new maps of SSR and PV power prepared here should support PV investments within the Chinese territory. |
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