Development of a sky imaging system for short-term solar power forecasting

Abstract. To facilitate the development of solar power forecasting algorithms based on ground-based visible wavelength remote sensing, we have developed a high dynamic range (HDR) camera system capable of providing hemispherical sky imagery from the circumsolar region to the horizon at a high spatial, temporal, and radiometric resolution. The University of California, San Diego Sky Imager (USI) captures multispectral, 16 bit, HDR images as fast as every 1.3 s. This article discusses the system design and operation in detail, provides a characterization of the system dark response and photoresponse linearity, and presents a method to evaluate noise in high dynamic range imagery. The system is shown to have a radiometrically linear response to within 5% in a designated operating region of the sensor. Noise for HDR imagery is shown to be very close to the fundamental shot noise limit. The complication of directly imaging the sun and the impact on solar power forecasting is also discussed. The USI has performed reliably in a hot, dry environment, a tropical coastal location, several temperate coastal locations, and in the great plains of the United States.

[1]  Margaret C. Anderson Studies of the Woodland Light Climate: I. The Photographic Computation of Light Conditions , 1964 .

[2]  Kenro Miyamoto,et al.  Fish Eye Lens , 1964 .

[3]  Janet Shields,et al.  Automated Visibility & Cloud Cover Measurements with a Solid State Imaging System , 1989 .

[4]  O. Faugeras Three-Dimensional Computer Vision , 1993 .

[5]  Steve Mann,et al.  ON BEING `UNDIGITAL' WITH DIGITAL CAMERAS: EXTENDING DYNAMIC RANGE BY COMBINING DIFFERENTLY EXPOSED PICTURES , 1995 .

[6]  B. Holben,et al.  Use of sky brightness measurements from ground for remote sensing of particulate polydispersions. , 1996, Applied optics.

[7]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[8]  J. Shields,et al.  The Whole Sky Imager - A Year of Progress , 1998 .

[9]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[10]  I. Reda,et al.  Solar position algorithm for solar radiation applications , 2004 .

[11]  P. Debevec,et al.  Direct HDR capture of the sun and sky , 2004, International Conference on Computer Graphics and Interactive Techniques.

[12]  Janet Shields,et al.  Cloud and radiance measurements with the VIS/NIR Daylight Whole Sky Imager at Lindenberg (Germany) , 2005 .

[13]  Josep Calbó,et al.  Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images , 2006 .

[14]  R. Hill A lens for whole sky photographs , 2007 .

[15]  A Cazorla,et al.  Development of a sky imager for cloud cover assessment. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Victor L. Krabbendam,et al.  LSST IR camera for cloud monitoring and observation planning , 2008, Astronomical Telescopes + Instrumentation.

[17]  Andreas Macke,et al.  Estimation of the total cloud cover with high temporal resolution and parametrization of short-term fluctuations of sea surface insolation , 2008 .

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

[19]  César López,et al.  Automatic observation of cloudiness: analysis of all-sky images , 2012 .

[20]  L. Alados-Arboledas,et al.  Calibration of an all-sky camera for obtaining sky radiance at three wavelengths , 2012 .

[21]  VALIDATION AND ANALYSIS OF HRRR INSOLATION FORECASTS USING SURFRAD , 2012 .

[22]  Looking skyward to study ecosystem carbon dynamics , 2012 .

[23]  Juan Huo,et al.  Comparison of Cloud Cover from All-Sky Imager and Meteorological Observer , 2012 .

[24]  John Pye,et al.  Cloud tracking with optical flow for short-term solar forecasting , 2012 .

[25]  Ronald D. Blatherwick,et al.  Ground-based all-sky mid-infrared and visible imagery for purposes of characterizing cloud properties , 2013 .

[26]  J. Kleissl,et al.  Chapter 9 – Sky-Imaging Systems for Short-Term Forecasting , 2013 .

[27]  Hsu-Yung Cheng,et al.  Predicting solar irradiance with all-sky image features via regression , 2013 .

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

[29]  R. Perez,et al.  Chapter 2 – Semi-Empirical Satellite Models , 2013 .

[30]  Jan Kleissl,et al.  EVALUATION OF NUMERICAL WEATHER PREDICTION FOR SOLAR FORECASTING , 2013 .

[31]  Jan Kleissl,et al.  A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting , 2013 .

[32]  Janet E. Shields,et al.  Day/night whole sky imagers for 24-h cloud and sky assessment: history and overview. , 2013, Applied optics.

[33]  Development of a sky imaging system , 2014 .

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

[35]  Jan Kleissl,et al.  Comparison of Solar Power Output Forecasting Performance of the Total Sky Imager and the University of California, San Diego Sky Imager , 2014 .

[36]  Jan Kleissl,et al.  Sky camera geometric calibration using solar observations , 2016 .