Development of a Short-Term Forecast System for Solar Surface Irradiance Based on Satellite Imagery and NWP Data
The increasing use of renewable energies as a source of electricity has lead to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on its load flows and associated with this economic effects....
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|The increasing use of renewable energies as a source of electricity has lead to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on its load flows and associated with this economic effects. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time periods (0-4 h) has grown. In this study, a novel approach for forecasting solar surface irradiance is developed which is based on the optical flow of the effective cloud albedo and SPECMAGIC NOW. This short-term forecast is combined seamlessly with the numerical weather prediction (NWP) to expand the forecast horizon up to 12 h. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced by extrapolating the optical flow into the future, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 15 min up to 4 h with a resolution of 0.05°. The combination of the displacement of clouds by TV-L1 and the calculation of solar surface irradiance by SPECMAGIC NOW is innovative and promising. Finally, a procedure for a seamless blending between a NWP model and the presented nowcasting is developed. For this purpose the software tool ANAKLIM++ is utilized which was originally designed for the efficient assimilation of two-dimensional data sets using variational approach. ANAKLIM++ blends the nowcasting, ICON and IFS between 1-5 h in such a way that the combined forecast delivers a smaller forecast error than the individual forecasts for each lead time.