Nowcasting of DNI Maps for the Solar Field Based on Voxel Carving and Individual 3D Cloud Objects from All Sky Images

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 Almeria (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.

[1]  P. Ineichen,et al.  A new airmass independent formulation for the Linke turbidity coefficient , 2002 .

[2]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[3]  Roland Siegwart,et al.  A Toolbox for Easily Calibrating Omnidirectional Cameras , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  J. Kleissl,et al.  Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed , 2011 .

[5]  Hao Huang,et al.  Correlation and local feature based cloud motion estimation , 2012, MDMKDD '12.

[6]  C. W. Chow,et al.  A method for cloud detection and opacity classification based on ground based sky imagery , 2012 .

[7]  Stefan Wilbert,et al.  Cloud shadow maps from whole sky imagers and voxel carving , 2015 .

[8]  Serge J. Belongie,et al.  Cloud motion and stability estimation for intra-hour solar forecasting , 2015 .

[9]  Dong Huang,et al.  3D cloud detection and tracking system for solar forecast using multiple sky imagers , 2015 .

[10]  Stefan Wilbert,et al.  Application of Whole Sky Imagers for Data Selection for Radiometer Calibration , 2016 .

[11]  Robert Pitz-Paal,et al.  Modeling beam attenuation in solar tower plants using common DNI measurements , 2016 .

[12]  Stefan Wilbert,et al.  Uncertainty of rotating shadowband irradiometers and Si-pyranometers including the spectral irradiance error , 2016 .

[13]  Robert Pitz-Paal,et al.  Shadow camera system for the generation of solar irradiance maps , 2017 .

[14]  Stefan Wilbert,et al.  Short-term forecasting of high resolution local DNI maps with multiple fish-eye cameras in stereoscopic mode , 2017 .

[15]  Robert Pitz-Paal,et al.  Validation of an all‐sky imager–based nowcasting system for industrial PV plants , 2018 .