Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.
Real-time solar energy resources mapping is crucial for the development and management of solar power facilities. This study analyzes the effects of the digital elevation model (DEM) resolution on the accuracy of the surface insolation (insolation hereafter) calculated by the Korea Meteorological Administration solar energy mapping system, KMAP-Solar, using two DEMs of different resolutions, 1.5 km and 100 m. It is found that KMAP-Solar yields smaller land-mean insolation with the fine-scale DEM than the coarse-scale DEM. The fine-scale DEM reduces biases by as much as 32 Wm− 2 for all observation sites, especially those in complex terrain and that the insolation error reduction is correlated with the difference in sky view factor (SVF) between the coarse- and fine-scale DEM. Both the coarse- and fine-scale DEMs generate the insolation-elevation and insolation-SVF relationship which is characterized by positive (negative) correlation between the insolation and the terrain altitude (SVF). However, the coarse-scale DEM substantially underestimates these relationships compared to the fine-scale DEM, mainly because the coarse-scale DEM underrepresents large terrain slopes and/or small SVFs, most seriously in high-altitude regions. The fine-scale DEM generates a more realistic insolation distribution than the coarse-scale DEM by incorporating a wider range of key terrain parameters involved in determining insolation. Improvements of insolation calculations in KMAP-Solar using a fine-scale DEM, especially in the areas of complex terrain, is of a practical value for Korea because the operational solar resources map from KMAP-Solar supports solar energy research, solar power plant installations, and real-time prediction and management of solar power within the power grid.
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