Robust cloud motion estimation by spatio-temporal correlation analysis of irradiance data

Abstract The main contributor to spatio-temporal variability in the solar resource is clouds passing over photovoltaic (PV) modules. Cloud velocity is a principal input to many short-term forecast and variability models. In this paper spatio-temporal correlations of irradiance data are analyzed to estimate cloud motion. The analysis is performed using two spatially and temporally resolved simulated irradiance datasets generated from large eddy simulation. Cloud motion is estimated using two different methods; the cross-correlation method (CCM) applied to two or a few consecutive time steps and cross-spectral analysis (CSA) where the cloud speed and direction are estimated by cross-spectral analysis of a longer time series. CSA is modified to estimate the cloud motion direction as the case with least variation for all the velocities in the cloud motion direction. To ensure reliable cloud motion estimation, quality control (QC) is added to the CSA and CCM analyses. The results show 33% (52°) and 21% (6°) improvement in the cloud motion speed (direction) estimation using the modified CSA and CCM over the original methods (without QC), respectively. In general, CCM results are accurate for all the different cloud cover fractions with average relative mean bias error (rMBE) of cloud speed and mean absolute error of cloud direction equal to 3% and 3°, respectively. For low cloud cover fractions, CSA estimates the cloud motion speed and direction with rMBE and mean absolute error equal to 10% and 11°, respectively. However, for high cloud cover fractions and unsteady cloud speed, CSA results are not reliable for 3–4 h time series; however, splitting the whole time series into shorter time intervals reduces the rMBE and mean absolute error to 15% and 16° respectively.

[1]  Jan Kleissl,et al.  Deriving Cloud Velocity From an Array of Solar Radiation Measurements , 2012 .

[2]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[3]  T. Hoff,et al.  Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance , 2012 .

[4]  Thomas Reindl,et al.  Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage , 2014 .

[5]  Francisco J. Batlles,et al.  Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast , 2013 .

[6]  Jan Kleissl,et al.  Spatiotemporal interpolation and forecast of irradiance data using Kriging , 2017 .

[7]  W. Menzel,et al.  Cloud Tracking with Satellite Imagery: From the Pioneering Work of Ted Fujita to the Present , 2001 .

[8]  T. Hoff,et al.  QUANTIFYING PV POWER OUTPUT VARIABILITY , 2010 .

[9]  A. Hammer,et al.  Short-term forecasting of solar radiation: a statistical approach using satellite data , 1999 .

[10]  Jan Kleissl,et al.  Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego , 2014 .

[11]  Thomas Reindl,et al.  Block Matching Algorithms: Their Applications and Limitations in Solar Irradiance Forecasting , 2013 .

[12]  Carlos F.M. Coimbra,et al.  Cloud-tracking methodology for intra-hour DNI forecasting , 2014 .

[13]  J. Mecikalski,et al.  Application of Satellite-Derived Atmospheric Motion Vectors for Estimating Mesoscale Flows , 2005 .

[14]  Bing Zeng,et al.  A new three-step search algorithm for block motion estimation , 1994, IEEE Trans. Circuits Syst. Video Technol..

[15]  C. Coimbra,et al.  Intra-hour DNI forecasting based on cloud tracking image analysis , 2013 .

[16]  L. Marroyo,et al.  Power output fluctuations in large scale pv plants: One year observations with one second resolution and a derived analytic model , 2011 .

[17]  Carlos F.M. Coimbra,et al.  Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs , 2013 .

[18]  Jan Kleissl,et al.  Cloud speed impact on solar variability scaling – Application to the wavelet variability model , 2013 .

[19]  J. Kleissl,et al.  Cloud motion vectors from a network of ground sensors in a solar power plant , 2013 .

[20]  Jan Kleissl,et al.  Cloud speed sensor , 2013 .

[21]  E. Arias-Castro,et al.  A Poisson model for anisotropic solar ramp rate correlations , 2014 .

[22]  P. Koepke,et al.  Optical Properties of Aerosols and Clouds: The Software Package OPAC , 1998 .

[23]  T. Hamill,et al.  A short-term cloud forecast scheme using cross correlations , 1993 .

[24]  J. Kleissl,et al.  Factors Controlling Stratocumulus Cloud Lifetime over Coastal Land , 2016 .

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

[26]  Azriel Rosenfeld,et al.  A Fourier approach to cloud motion estimation , 1978 .

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

[28]  J. Leese,et al.  An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross Correlation , 1971 .

[29]  Richard Perez,et al.  Chapter 10 – SolarAnywhere Forecasting , 2013 .

[30]  Petros Maragos,et al.  Motion displacement estimation using an affine model for image matching , 1991 .