Accurate nowcasts of the direct normal irradiance (DNI) for the next 15 min ahead can enhance the overall efficiency of concentrating solar power (CSP) plants. Such predictions can be derived from ground based all sky imagers (ASI). The main challenge for existing ASI based nowcasting systems is to provide spatially distributed solar irradiance information for the near future, which considers clouds with varying optical properties distributed over multiple heights. In this work, a novel object oriented approach with four spatially distributed ASIs is presented. One major novelty of the system is the application of an individual 3D model of each detected cloud as a cloud object with distinct attributes (height, position, surface area, volume, transmittance, motion vector etc.). Frequent but complex multilayer cloud movements are taken into account by tracking each cloud object separately. An extended validation period at the Plataforma Solar de Almería (PSA) on 30 days showing diverse weather conditions resulted in an average relative mean absolute error (relMAE) of around 15 % for a medium lead time of 7.5 minutes and a temporal average of 15 minutes. Further reductions of the relMAE were achieved by spatial aggregation, with a relMAE of 10.7 % for a lead time of 7.5 minutes, a field size of 4 km² and a temporal average of 1 minute (during one day). Nowcasting systems described in the literature reach similar deviations but were often validated only for a few days based on a single ground measurement station, which confirms the good performance and the high applicability of the presented system. Three implementations of the system exist already demonstrating the market maturity of the system.
Nowcasting of DNI maps for the solar field based on voxel carving and individual 3D cloud objects from all sky images
Bijan Nouri, Pascal Kuhn, Stefan Wilbert, Christoph Prahl, Robert Pitz-Paal, Philippe Blanc, Thomas Schmidt, Zeyad Yasser, Lourdes Ramirez Santigosa, Detlev Heineman; Nowcasting of DNI maps for the solar field based on voxel carving and individual 3D cloud objects from all sky images. AIP Conf. Proc. 8 November 2018; 2033 (1): 190011. https://doi.org/10.1063/1.5067196
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